Scheduling and energy – Industrial challenges and opportunities

Accepted Manuscript Title: Scheduling and energy–Industrial challenges and opportunities Author: Lennart Merkert Iiro Harjunkoski Alf Isaksson Simo Saynevirta Antti Saarela Guido Sand PII: DOI: Reference:

S0098-1354(14)00176-8 http://dx.doi.org/doi:10.1016/j.compchemeng.2014.05.024 CACE 4977

To appear in:

Computers and Chemical Engineering

Received date: Revised date: Accepted date:

23-1-2014 18-5-2014 21-5-2014

Please cite this article as: Merkert, L., Harjunkoski, I., Isaksson, A., Saynevirta, S., Saarela, A., and Sand, G.,Scheduling and energyndashIndustrial challenges and opportunities, Computers and Chemical Engineering (2014), http://dx.doi.org/10.1016/j.compchemeng.2014.05.024 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

*Highlights

SCHEDULING AND ENERGY - CHALLENGES AND OPPORTUNITIES

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The differences between energy efficiency and demand-side management is clarified Major challenges of combining scheduling and energy management are shown Different approaches for energy-aware scheduling are shown and compared Real world use cases of different industries are presented

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Highlights:

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*Manuscript (for review)

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SCHEDULING AND ENERGY – INDUSTRIAL CHALLENGES AND OPPORTUNITIES

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Lennart Merkerta, Iiro Harjunkoskia*, Alf Isakssonb, Simo Saynevirtac, Antti Saarelac and Guido Sanda a) ABB AG, Corporate Research Center, Industrial Software and Applications, Wallstadter Str. 59, 68526 Ladenburg, Germany (e-mail: [email protected], tel.: +49 6203 716014) b) ABB AB, Corporate Research, 72178 Västerås, Sweden c) ABB Oy, Strömbergintie 1 B, 00380 Helsinki, Finland

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Abstract

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Recent developments in energy markets, such as the increasing share of inherently volatile renewable power in the energy supply mix and the need of reducing carbon emissions while improving the production efficiency, make the operating environment of process industries more dynamic and complex. At the same time, continued advances in the mathematical programming and IT technologies open up new opportunities to tackle the related operational scheduling problems in a more integrated way at an ever larger scale. This paper discusses the industrial challenges arising from the deregulation of the electricity markets and stonger presence of unpredictable renewable energy sources. It gives a brief overview of methods currently available followed by set of real industrial case studies. The paper concludes with a discussion of the main challenges and opportunities relevant to the presented examples..

Scheduling, energy efficiency, demand side management, production planning, thermo-mechanical pulp, steel production, byproduct gas network optimization

1. Introduction

The increase of renewable (wind, solar, biomass, etc.) energy generation is supported by many governmental programs and is e.g. reflected in the US NSF program “Energy for Sustainability” and EU frame program “Horizon 2020”. It is seen as something inevitable for which the technology should be developed although the political or commercial solution has not yet been fully established. In Europe, for instance the decision of the German government to close down the nuclear power plants poses a big challenge to the power grid and end users, along the increasing share of wind- and solar power. Instead of having a stable base supply, the fluctuations of the energy availability will increase dramatically. Energy and price forecasting taking into account a number of factors such as weather and demand forecasts are becoming more important. Especially large consumers who nowadays have to forecast their consumption will get

more and more an important role in stabilizing the power grid. At first sight short-term production planning and energy management seem to have competing goals. However when both are integrated in a collaborative way, short-term production planning is an enabler for the participation on energy markets and stabilization of the power grid. Short-term production planning – or scheduling – has gained lot of attention in the last decades. As a technology it has evolved both on the modeling as well as on the algorithmic front. Better and more compact models can today address problems of industrial relevance owing to reduced integrality gaps and new process-related features among the multitude of modeling options (e.g. State-TaskNetwork, Resource-Task-Network, event- and precedence based continuous-time methods). It is natural that the energy consumption of production processes also becomes

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decades also improved the size of solvable problems significantly resulting in a performance improvement factor of more than a million with regards to the solution time. Nevertheless, even if the solution of MILP problems has become much faster, the computational performance limitations still pose the main problem for the modeler. Better formulated models are the key to achieve optimal solutions to large-scale problems. Thus, well formulated models nowadays give industry the opportunity to do EWO combining production planning and energy management aspects to guarantee a more efficient and sustainable production.

2. Energy efficiency Management

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Demand-side

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Global energy consumption is expected to grow continuously in the future (Conti & Holtberg, 2011). In industries such as metals, pulp and paper, cement or air separation, energy is an important cost factor. Reducing this cost is of high interest, especially since energy costs are rising due to increasing energy demand and changes in the generation mix and supply. In many countries reduction of CO2-emissions is on the political agenda (European Union, 2011). Energy cost reductions at an individual processing plant can often be achieved by shaping the load profiles of electricity consumed. Figure 1 shows examples on how load curves can be influenced.

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part of the operational scheduling problem, especially since with emerging renewables and smart grids the exact time of consumption may significantly affect the price for the electricity. It is also now technically possible to already in the production scheduling phase take into account the varying electricity prices for the next days. The joint consideration of earlier isolated problem aspects requires new ways of formulating and solving the integrated problems. Enterprise-wide Optimization (EWO) is a relatively new research field (Grossmann, 2005) that combines various aspects of production. The main idea is to plan the production simultaneous considering aspects from the control, scheduling and supply chain layers such that the overall results support the operations of an enterprise. This avoids the risk of having competing optimization solutions resulting in no improvements. This field has triggered new opportunities and put higher focus on various challenges. Energy consumption of production processes and the dynamics of the power grid are aspects that suite well the philosophy of EWO. The main challenge is to ensure that the production scheduling model encapsulates the immediate effect of a scheduling decision on the energy consumption as part of the optimization problem. This resembles the challenge of integration between scheduling and control (Engell and Harjunkoski, 2012), where different dynamics including the inertia of a process must be optimally combined. The concept of EWO can of course be enlarged without theoretical limitations, nevertheless the solvability of large-size optimization problems is still limited, mainly due to a combinatorial explosion. The problems involve logic decisions that can be modeled using binary variables and the number of alternative combinations of a solution grows exponentially. Nevertheless, the topic of integrating energy and scheduling per se is not new. A number of contributions of heat integration and scheduling can be found in literature (Papageorgiou et al, 1994; Georgiadis and Papageorgiou, 2001; Halim and Srinivasan, 2009; Seid and Majozi, 2014) where also energy storage and water re-use are considered. Many of the contributions in heat integration are in fact related to process design (Barbosa-Póvoa et al., 2001;Stamp and Majozi, 2011; Diwekar and Shastri, 2011), whereas here the focus is on the operational scheduling. A recent review of the major contributions in energy handling is given in Fernández et al. (2012). Even thought some technological advancements allow it, most of these contributions do not, however, consider continuously varying energy or electricity prices, which is a major topic and challenge for the energy-intensive industry of today. One of the technologies that might tackle the energy problem in operations is Mixed-integer Linear Programming (MILP). MILP solvers have improved significantly in the last decades and can today solve several magnitude larger problems than before. The progress in hardware development within the last two

Figure 1: Load shapes (Source: Charles River Associates, 2005)

2.1. Energy efficiency Increasing the energy efficiency is a straightforward way to reduce the specific energy consumption and subsequently the total energy costs. The goal is to reduce the overall energy consumption without changing the production volume of the site. Some typical actions to increase the energy efficiency are: Install more energy efficient equipment, e.g. LEDs instead of normal lighting

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Peak clipping or load shedding at peak times Load shifting from peaks to valleys

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Demand-side Management is of very high interest to utilities and grid operators as electricity cannot be stored in the power grid. Supply of electricity has to equal consumption within the grid at any point in time and follow load changes. Eventhough the highest consumption peaks are relatively rare throughout the year utilities need to provide backup power plants for these. Maintaining these rarely used plants is very expensive, so utilities are interested in reducing the height and occurrence of consumption peaks in order to reduce the need for backup power plants. Normally electricity consumers do not have any interest in tracking if there is a peak in the consumption or not. To motivate them to reduce consumption at peak times, there are several approaches which can be classified into two different types (NERC, 2007; Baboli et. al., 2011):

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Many other energy efficiency actions are possible as well. However, for implementing energy efficiency actions at industrial sites the potential benefits need to be proven and verified upfront. One measure to track energy efficiency is energy consumption per ton of product delivered but other measures might be more applicable and the actual measures for energy efficiency need to be chosen wisely to maximize the impact of the improvement actions.

applied to other forms of energy. The methods discussed in section 4 consider all forms of energy together with production scheduling. As Figure 1 shows, there are several ways to influence the demand side load curve. A key difference to energy efficiency is that Demand-side Management is not mainly about reducing the overall consumption, but about consuming or not consuming at the right time. The main options for Demand-side Management are (as shown in Figure 1):

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Replace fixed speed pumps followed by a valve with variable speed pumps Insulate buildings better in order to decrease heat losses Define better operating strategies leading to fast and easy consumption reductions, e.g. switch off equipment during unproductive times Detect inefficient operations early, e.g. leakage in pipeline systems leading to inefficient pumping and material losses Improve material efficiency by reducing process waste through better planning and control of the production processes

Figure 2: The Plan-Do-Check-Act cycle for continuous improvement (Source: Olsen, Goli and McKane, 2012)

An energy management system can measure and track energy consumption and is a crucial tool to calculate and verify the benefits of energy efficiency actions. ISO 50001 is a standard providing the guidelines for such systems (ISO, 2011). This standard introduces a continuous improvement cycle; the plan-do-check-act cycle (Figure 2), according to which each and every action needs not be only planned and implemented, but their effect should also be controlled in order to derive better follow up actions. The first step to start the continuous improvement process is to install a monitoring system which measures the actual energy consumption. Based on the information gathered with this monitoring possible actions to reduce energy consumption can be evaluated.

Dispatchable / Incentive based programs Non-dispatchable / price based programs In dispatchable or incentive based programs the utility or grid operators typically pays the electricity consumer to get direct control on shedding some non-crucial loads. The consumer gets a compensation for offering this service. In non-dispatchable or price-based programs the price of electricity fluctuates during the day. Most common are Time-of-Use tariffs, e.g. day/night-tariffs and Critical Peak Pricing, where consumption peaks are billed with additional fees. Real-time prices which are often linked to hourly spot market prices are today used mainly for very large consumers.

2.2. Demand-side Management Demand-side Management (also known as DemandResponse) refers to different ways of shaping the energy demand curve at consumer side. It is currently mainly applied to electricity. However, it can also be

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3. Scheduling in industry

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Figure 3: Classification of demand response programs (Source: Baboli et. al., 2011)

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Production scheduling plays an important role in most manufacturing and service industries (Pinedo, 1999). Scheduling problems can be found in a wide range of industries, for instance in pulp & paper, metals, oil & gas, chemicals, food & beverages, pharmaceuticals, transportation, service and military. In all of these a set of given tasks need to be performed on specified and often limited resources. Often this involves exact planning of equipment, material, utilities and personnel to ensure that they are available at the right locations when needed. The main decisions comprise which tasks to execute (batching, selection, prioritization), where to process them (equipment assignment), in which order (job sequencing) and when to execute the tasks (exact timing). The time is, interestingly, also a limited resource, which has resulted in a large number of approaches with various time-concepts to solve the resulting scheduling problems. A number of scheduling review papers can be found summarizing the most promising approaches, for instance Floudas and Lin (2004), Mendéz et al. (2006), Li and Ierapetritou (2008) and Maravelias (2012). There are relatively few contributions focusing on the industrial aspects of scheduling. Emerging challenges are identified in Henning (2009). Framinan (2012) focuses on deployment and implementation of scheduling systems and recently, Harjunkoski et al. (2014) discusses some important practical aspects and lessons learnt from industry. The most common way to perform the task of production scheduling in the industry is still doing it manually by experienced planners using pen and paper, planning cards e.g. Kanban or spreadsheets. As the production landscape is becoming more and more complex many companies have identified the need for more systematic and optimal methods in order to meet their specific production targets. The complexity is increased due to higher production volumes, more individual products increasing the product portfolio and volatile customer orders and high pressure to save on production and energy costs. A good scheduling solution with optimization capabilities can result in significant savings through better capacity utilization, which not only brings economic benefits but can also help reduce the environmental load through decreased or better controlled energy demand. Efficient tools can also help coping more efficiently with uncertainties in production. Production scheduling is only a part of a decision chain, normally organized as part of the production management layer (see Figure 5). The production targets are often defined by an enterprise resource planning (ERP), and realized in a manufacturing execution system (MES) or collaborative production management (CPM) portfolio, both of which are responsible for communicating the decisions to the process, follow the production and take into account expected and unexpected changes in production (tracking and rescheduling).

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Besides the classification into dispatchable or nondispatchable programs, Demand-side Management activities can also be distinguished by the time scale they address. As shown in Figure 4, demand side management can act within seconds similar to a spinning reserve or within a range of hours, days or weeks such as daily energy efficiency and time-of-use programs (Demand Response Research Center, 2013). Dispatchable/incentive based programs are in general capable of acting on faster time scales than non-dispatchable/price based programs.

Figure 4: Different time scales for demand response (DR) (Source: Demand Response Research Center, 2013)

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to 10 minutes. Normally tasks are only allowed to start at a grid point but can finish at any point of time. Here, the main scheduling decision is to assign the start of each operation to the correct grid point, which is represented by a binary variable. Some of the best known approaches are state-task networks (STN) by Kondili et al. (1993) and Shah et al. (1993) and resource-task networks (RTN) by Pantelides (1994). There are also a number of contributions to further enhance the functionality of STNs (e.g. Maravelias and Grossmann, 2003b) but the basic formulation has stayed more or less the same.

Task / Job

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Depending on the type of processes, different methods can be used to solve the resulting scheduling problems. Mathematical programming is one of the best ones to guarantee a global optimality, i.e. the best possible solution. However, most optimization methods become eventually slow when the problem size grows and therefore a large number of heuristics (backward scheduling, forward scheduling, shifting bottleneck etc.) and methods that combine for instance manual decisions through interactive Gantt charts with automated methods have been developed to tackle the daily industrial problems. Especially two decades ago, lots of efforts were put on developing expert systems that imitate the human behavior, evolutionary algorithms and different artificial intelligence (AI) methods.

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Figure 6: Discrete-time approach (equidistant time grids)

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Figure 5: Decision layers in production (Source: ISA-95 standard)

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The continuous-time approach has been developed earlier in the OR-literature. Here decisions can take place anywhere on a continuous time-axis (Figure 7). Thus, the main scheduling decision turns into deciding in which sequence to perform the tasks. The precedence variables are binary. Some basic continuous-time formulations include cyclic and slot-based approaches by Pinto and Grossmann (1994, 1995), global event point approach by Ierapetritou and Floudas (1998), continuous time RTN formulation by Castro et al. (2001) and improvements on global precedence Mendez and Cerda (2003). Many improvements to these have been published ever since and in the context of this paper we only want to highlight some of them (for more details, we refer to the aforementioned review papers). Maravelias and Grossmann (2003a) further developed the STN toward continuous-time and a number of computational improvements have been recently proposed e.g. by Velez and Maravelias (2013) and Merchan et al. (2013). The event point methods have been improved in a substantial number of publications significantly reducing the integrality gap (Janak and Floudas, 2008), and introducing tightening constraints (Li, Xiao and Floudas, 2012). Improvements on the continuous-time RTN formulations have been proposed e.g. by Castro et al. (2011) introducing electricity constraints. A unification of RTN/STN models was recently proposed by Shaik and Vooradi (2013). Other continuous-time scheduling approaches include the unit slots by Susarla et al. (2010), which reduces the necessary number of event points. The approach is expanded to semi-continuous plants using a multi-grid formulation (Susarla et al., 2012).

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By far the most applied mathematical programmingbased methods deploy mixed-integer linear programming (MILP) techniques. The first scheduling models come from the operations research (OR) community, e.g. the continuous-time model by Greenberg (1968), but since three decades it has become a major research field within engineering – often chemical engineering. This is a result of the above mentioned algorithmic and hardware improvements that enable the solution of optimization problems within a reasonable time. Consequently, the engineering community has tried to enhance the practical problem representations and formulate many of the problems encountered at the plant floor, which also has resulted in a continuously growing problem complexity and size. Recent approaches to consider the uncertainty and robustness (see e.g. Verderame et al, 2010) on the other hand offers more functionality but simultaneously further increase the problem complexity. Today, production scheduling is an integral part of operational decision making – the question is not whether to do it or not but how to perform this central function and to what level of optimality. 3.1. Time representation The most characteristic property of methods for solving a scheduling problem is the time representation. Discrete-time approach divides the time horizon into evenly or non-evenly distributed time grids, at which decisions may take place. Figure 6 shows a task on a discretized time grid, where each grid point is equivalent

Task / Job

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Figure 7: Continuous-time approach

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Figure 8: Time decisions of tasks on an equipment

3.2. Scheduling problem formulation Having a set of tasks or operations to be performed on a set of finite resources, the main decisions of a scheduling problem formulation are:

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This equation avoids time-based overlaps of equipment utilization between tasks. It requires a priori knowledge of the processing time ptij. Equation (1) proposed by Shah et al. (1993) is the basis for the STN formulation, which further can contain constraints for capacity limitations, material and resource balances and sequence-dependent changeover times. For more details, we refer to the original paper or a review paper e.g. Mendez et al. (2006). Another discrete-time approach is the resource-tasknetwork formulation by Pantelides (1994). From the main principles it is similar to the above STN-approach with the main simplification that all resources are treated similarly in a resource-balance equation (Eq. 5). This reduces the amount of equations needed and the consumption of resources can be discrete (equipment), i.e. resources are consumed in the beginning and given back at the end of a batch processing step, or continuous (e.g. electricity). The formulation can efficiently model identical equipment using only one binary variable and typical other equations define the resource constraints, such as for example the maximum size of an intermediate storage silo. For more details we refer again to Mendez et al. (2006) and the example in the next section. Continuous-time approaches offer an exact time representation (if desired on a millisecond exactness) without adding to the problem complexity. Also, all timings (production, setup and transfer) can be variable with provided lower and upper bounds, possibly restricted through constraints. The main drawback of continuoustime approaches, especially in precedence-based formulations, is the difficulty of formulating balances, as there are no clearly defined balance points. One of the main approaches to overcome this gap is to introduce event points (Ierapetritou and Floudas, 1998) and enhance the STN/RTN concepts over continuous time (Maravelias and Grossmann, 2003a; Castro et al., 2001). The combinatorial complexity of continuous-time problems normally grows steep with the number of tasks, and due to a weak relaxation gap caused by big-M constraints it may be difficult to find good solutions within a reasonable time. To illustrate this, assume a binary variable equals one if task i precedes task j, else zero. Additionally assume an assignment binary variable that takes the value one if task i is processed on equipment m. With these variables one of the conditions in Fig. 8 can be modeled. First we assume that each task can be assigned only to one optional resource (Eq. 2). Further we assume that only one (global) sequence can take place (Eq. 3). The disjunction in Figure 8 is that either task 1 can be processed before task 2 or vice versa. This is realized by Eq. (4), where M is a sufficiently large number and and are the respective start and finishing times of task i

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Timing of operations (start, end, duration): Either continuous or discrete variables Sequencing of operations: Binary variables for continuous-time, constraints in discrete-time formulations Assignment of tasks to equipment variables: Binary variables in continuous-time, embedded in the grid decision binary variable in discrete time Various constraints for assignment, sequencing, inventory, etc. Objective function providing the cost function for the decision variables.

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The most common target of scheduling is to use the resources as efficiently as possible simultaneously ensuring a practical and feasible scheduling that minimizes the production- and operational costs. One of the first aspects of feasibility is to avoid overlapping of tasks on an equipment, which can be done through the variables defining the timing (Figure 8). In continuous-time approaches these are typically continuous variables, whereas in discrete-time formulations they depend on the time grid and binary assignment variable to these.

The main differences between the above two types of approaches are whether decisions are formulated as variables, constraints or combination of these. In discrete-time approaches it is straightforward to make balance equations around each grid point, which are well defined and this allows straightforward integration of energy and other material / inventory constraints. Nevertheless, the accuracy of the problem representation may be insufficient, as the problem complexity and size grow with the number of grid points needed to represent the time axis. Also, in most discrete-time approaches it is assumed that the processing- and all other times are constant and given as parameters. If the times are smaller than a grid interval, they may need to be rounded up (or will be by the formulation) and thus the production capacity may be underestimated. As an example assume that the binary variable if a task i starts on equipment j at time point t. Consequently, an overlap of two tasks can be avoided through Eq. (1), which ensures that only one of the tasks within their processing times can be active per equipment.

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i

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yij  y ji  1 ij | i  j

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setup t sjm  timf  Tijm  M 1  yij   M 2  xim  x jm 

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4.2. Reduction of energy consumption with better coordination Improving the energy efficiency of a particular production system means to reduce its overall energy consumption without reducing the production (see section 2.1). In cases where insufficient coordination of production plans of subsequent production stages causes avoidable energy losses, production scheduling can help to improve the energy efficiency of an enterprise. Here, intelligent production planning that reduces buffers in production directly translates into avoiding (thermal) energy losses. There are many important contributions in the literature on handling of intermediate storage tanks and on heat integrated scheduling in batch plants: Adekola et al. (2013) for instance present a unified approach for the optimization of energy and water in multipurpose batch plants. They use a flexible scheduling framework that enables optimization of the schedule together with water and energy usage simultaneously. Stamp & Majozi (2011) consider the heat integration together with optimisation of heat storage size for multipurpose batch plants. Halim & Srinivasan (2009) do also look into the problem of incorporating heat integration in batch process scheduling. They developed a decomposition algorithm based on sequentially solving the scheduling problems and heat integration problem. More generally, Shaik & Floudas (2007) improved a unit-specific event-based continuoustime model for short-term scheduling of continuous processes by a rigorous treatment of storage requirements. A commonality of the aforementioned approaches is that they do not take existing optimization and planning functions into account. The elimination of buffers needs to be supported by a revision of an existing design of the Page 8 of 21

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These equations are of course not complete but show one of the main challenges: formulating and solving scheduling problems give rise to large problem sizes even if the modeling principles are relatively logical and simple. It is still a major challenge, despite the many significant computational improvements, to solve industrial-scale scheduling problems to optimality (Harjunkoski et al., 2014). This is true partly because no general rescheduling framework exist for a larger class of problems, which is a key feature in an continuously changing industrial environment. Due to this the best approach today is probably to combine a robust mathematical model with some kind of supporting heuristics, such as rolling horizon or decomposition techniques.

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Loose couplings of production stages are typically exploited during the design of the operations and control strategies of the processing system: The couplings are neglected in the first step and control and operational strategies are designed for the particular stages while neglecting the neighboring stages. In the second step, existing couplings with the neighboring stages are considered as external inputs or disturbances. The price of this simplified approach is that processes in the buffers are usually not controlled or optimized explicitly. Operating and controlling different process steps and the buffers in between in a common framework is one way to achieve a more Enterprise Wide Optimization. Buffers in production processes and supply chains increase the operational flexibility and robustness but do not come for free. Material in buffers ties up capital. In case the material is stored and processed at high or low temperatures thermal energy losses occur. Furthermore, buffers decrease the responsiveness of a production process or a supply chain. In an increasingly global and competitive market environment, customers look for higher flexibility, faster delivery, and lower cost from producers. These requirements call for higher degrees of process integration and reduced buffers.

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4. Scheduling production energy consumption

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Production processes in single stage production very often do not run 24 hours a day, which allows flexibility to adapt production to energy management needs. The same applies to multi-stage production processes as normally there are only a few bottleneck process steps.

4.1. Buffers in production processes and supply chains Multi-stage production processes and supply chains typically exhibit buffers to store raw material, intermediate and final products for limited periods of time. Buffers exist at all levels of an enterprise operation, starting from unit level with buffer tanks between unit operations, going through plant and site levels with in-bound and out-bound inventories in storage tanks and warehouses and all the way to the company and global supply chain level. The higher the capacity of these buffers are the lower is the degree of coupling between the stages and the higher is the degree of operational flexibility of the entire multi-stage system. Figure 9 shows an illustrative comparison of flow sheets with and without buffer (Sand and Terwiesch, 2013).

Figure 9: Process flow sheet with buffer (left) and with higher degree of integration (right)

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4.3. Improved energy efficiency by better coordination In the following we focus on a steel case study by Xu et al. (2012). The overall process consists of a number of sequential steps in which scrap and other raw material is processed to steel products. The first production step produces hot slabs of different grades, which are stored in a slab yard where they cool down over time. In the second step the slabs are reheated and further processed in a hot rolling mill into coils. It is obvious that long storage times lead both to high energy losses and storage costs. By reducing the buffer, i.e. reducing the amount of stored material between the production steps, the energy efficiency of the integrated steel production process can be improved. Each of the two production steps has its own planning and scheduling system (Figure 10). The systems are, however, not integrated on the schedule optimization level in the sense that the transfer times of material from one to the next processing step are considered as a joint degree of freedom in order to minimize the amount of stored material and their storage times.

scheduling systems leads to significantly different sequences of slabs poses a theoretical challenge. The optimization objectives of the two schedulers are different from each other such that the first step produces slabs in a sequence, which is often significantly different from the one in which they are consumed by the second step. This lack of coordination causes long storage times resulting in large energy losses through slab cooling and high storage costs. In order to improve the energy efficiency a better coordination between the stages, which takes the buffer explicitly into consideration, is necessary. A practical challenge emerges from the need to reuse the existing scheduling systems, which may come from different vendors, allow little structural modifications or only some parameter tuning. Companies often prefer a stepwise evolution (rather than a revolution) of their planning systems since it keeps the risk of technical infeasibility, non-acceptance and economic losses small. 4.3.1. Use Case: Coordination of steel melt shop scheduling and hot rolling mill scheduling Xu et al. (2012) studied an industrial-scale steel making process that consists of a melt shop as the first production step including a continuous caster, where the intermediate products, hot slabs, are produced in an optimized casting sequence and cooled down during the storage in the slab yard. In the second production step, hot rolling including a reheating furnace, the slabs need to be reheated in the reheating furnace to about 1250°C for the further processing in the hot rolling mill (Figure 11). Long storage times of slabs are expensive due to the huge energy consumption in the reheating furnace and the high value of the intermediate product. A stainless steel slab weighing 25 tons may have a value of around 50,000-75,000 EUR. To reheat such a slab from the ambient temperature to the hot rolling temperature (1250°C) typically requires 1000m³ of natural gas per ton. Both the melt shop scheduling problem and the hot rolling scheduling problem belong to the class of flexible flow shop scheduling problems: A set of n jobs has to be processed in a series of m stages optimizing a given objective function and at least one of the stages comprises two or more parallel machines. The melt shop consists of

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control and operational strategies of the overall processing systems. The integration of the process comes along with an integration of the corresponding existing control and operations management functionality. One challenge in industrial practice arises from the fact that often distinct control and operations management solutions from different vendors exist for the different stages of the production or the supply chain. Their integration into an overall integrated design might be hindered by their limited structural flexibility and their limited parameterizability.

Figure 10: Schedule coordination problem

The fact that the two independent production

Figure 11: Melt shop and hot rolling mill schedule coordination problem in the steel industry

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define for each slab (processed in job i) a latest due date for MSO Ci and an earliest release date for HSO Si.

Figure 12: Linking variable bi for the coordination approach

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The coordination aims at minimizing the sum of all slack times ei and li in addition to the make spans of the meltshop and the hot rolling schedules. Shortening the slack times means shortening the cool down times of the hot slabs such that energy costs for reheating are reduced. The overall solution concept is to solve the coordination problem as the master problem and the MSO and HSO as sub-problems in an iterative scheme. For the upper level coordination problem three different heuristics were compared based on simplified case studies: a heuristic based on Lagrangean decomposition (LD), a derivative-free optimization algorithm - Multilevel Coordinate Search (MCS), and a new intersection coordination heuristic (IC). The numerical comparisons show the advantage of the IC among these three coordination approaches in terms of solution quality and computational effort. The high computational costs of the evaluation of solutions by the MSO and the HSO made the use of LD and MCS unattractive and motivated to develop a new coordination approach to this schedule coordination problem. A bi-level decomposition-based approach with iterative cuts generation was developed, where the upper level coordinator is created using the knowledge on the bottlenecks in the production chain. It is based on an intersection model which includes variables and constraints of the last stage in the MSO problem and the first stage in the HSO problem. The cuts are generated based on the solution of the lower level schedulers MSO and HSO. Both MSO and HSO consider complex production constraints e.g. temperature requirements, chemistry,

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four production stages: electric arc furnaces (EAF), argon oxygen decarburization units (AOD), ladle furnaces (LF) and continuous casters (CC). Heats are processed through the production stages and finally cast into slabs (typically one heat results into 5-10 slabs) in a predefined casting sequence. The main scheduling objective is to group the heats in a suitable sequence and minimize the make span. The hot rolling mill comprises three production stages: reheating furnace (RF), hot strip mill (HSM), and power coil winding machine (PC). The slabs produced in the melt shop must be processed along these three production stages, in a given sequence. The main objective is to define the hot rolling sequence for the slabs (rolling programs) and minimize the make span. In general, already a scheduling problem with two processing stages, where only one stage contains two parallel machines has been proven to be NP-hard. ABB has developed two optimization-based schedulers, the melt shop scheduler (MSO) and the hot rolling scheduler (HSO) for a leading steel company. The MSO and HSO problems were formulated and solved as nested optimization problem consisting of several MILPsteps and heuristics. Currently the operators can generate an overall production schedule for the two sections either by a pull strategy, running the hot rolling mill scheduler first and then MSO, or by a push strategy, i.e. by first scheduling the melt shop and then performing HSO. In both strategies, one scheduler must adapt to the result of the other, and the potential of coordination, in particular the aspect of hot charging slabs into the reheating furnace (energy saving), is wasted. In the following coordination approach we assume that the MSO and HSO algorithms are black-box solvers, i.e. cannot be modified structurally but only parameterized to some extent. 4.3.2. Coordination approach and results Coordinating the distinct schedules of the two production steps, melt shop and hot rolling, requires an explicit representation of the storage times of the slabs in the slab yard. The idea is to introduce new linking variables bi along with constraints that correspond to the sub-problems, as depicted in Figure 12. The linking

Figure 13: Hourly reheating furnace energy consumption histogram of coordinated and uncoordinated overall schedule

variables can be interpreted as synchronization points that

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4.5. Discrete-time scheduling using RTN One of the very promising approaches to model and solve scheduling problems with energy constraints is provided by the resource task network (RTN) concept. The principle is very simple and thus also flexible as there are only two elements: resources and tasks. The key of the approach lies in resource balances that are built around the starting times of the tasks. Doing this using a discrete-time approach does in fact not change much, as the RTN has earlier also been used to take material and inventory balances into account. Energy differs from any other balance only in one aspect, i.e. the timing how frequently the balances must be checked depends on the grid operator (e.g. every 15 minutes or once per hour). Thus the additional complexity is only possible in a more dense grid but the basic formulation remains unchanged. The starting times of tasks are modeled with the discrete variable , which equals one if task starts at time-point . The resource balance is controlled through a continuous variable that stands for the resource level at each time point. The parameter is the amount of discrete consumption of resource at the beginning of the task and the amount of e.g. equipment, produced or released at the end of the task. For continuously consumed resources, e.g. electricity, the parameter is used for similar tracking. The duration of the task is given by the parameter and the generic resource balance is given as follows:

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4.4. Shifting loads with scheduling Buffers within production processes, as described in the beginning of chapter 4, can also be used in a different manner. Besides increasing the energy efficiency by a better coordination, scheduling also offers a potential to shift electric loads to less expensive times. Especially, if there is some storage for intermediate products available, non-bottleneck processing steps may offer some flexibility with regards to their processing times. This flexibility can be used to decrease energy cost by load shifting in combination with scheduling. A scheduling solution can consider time varying energy cost when planning the nonbottleneck processing steps to achieve energy cost minimal schedules. Since the bottleneck processes may still run at maximum productivity the overall plant productivity is not reduced. Scheduling of energy intensive batch processes also allows influencing the energy consumption load curve. Batches may have different energy intensities and between batches the maintenance and setup times with low energy

times into account an automated scheduling algorithm can create an energy cost minimal production plan without significantly decreasing productivity. Hence, time varying energy prices can be used to minimize overall energy cost of a production process.

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(e.g. order due dates), etc. The intersection coordination heuristic has been applied based upon these existing modules. Figure 13 shows the comparison of an overall production schedule with and without coordination for a problem instance comprising 300 slabs. The coordination improved the solution with regards to several aspects: The total energy consumption was reduced from 771 MWh to 626 MWh, the energy consumption peak of the reheating furnace was reduced by 13%, the average slab storage time was reduced by 23% and the makespan of the overall schedule (i.e. combined MSO and HSO schedule) decreased by 7%. For more details see the paper by Xu et al. (2012).

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Figure 14: The cement process in Castro et al. (2009)

consumption need to be considered. Taking different energy consumptions of batches and setup or maintenance

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network is illustrated in Figure 15. The corresponding resource balances can be “automatically” converted into constraints in the MILP problem. Interestingly, a continuous-time approach for scheduling the cement mill including varying energy prices was addressed two decades ago by Kondili, Shah and Pantelides (1993). In Castro et al. (2013) and Sun et al. (2013) the discrete-time RTN is used in a similar way to formulate and solve the problem of meltshop scheduling (Harjunkoski and Grossmann, 2001). The simplified RTNrepresentation is shown in Figure 16. The problem has some major differences to the above cement problem

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The last term accounts for resource interactions across the system border, e.g. electricity requirement from a provider (which cannot be stored). With this principle most scheduling problems can be formulated using the discrete time approach. In Castro et al. (2009) an approach for scheduling a continuous process (cement mill) is presented and as an entire week must be scheduled an aggregation approach is used to reduce the complexity. The process considered is shown in Figure 14. A new continuous-time formulation for short-term scheduling of continuous multiproduct plants under variable utility availability costs/profiles and multiple intermediate due dates was shown to be less efficient as the discrete-time counterpart. The respective resource-task

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Figure 15: Corresponding RTN graph of the cement problem (Castro et al., 2009)

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4.6. Energy cost constraints with continuous-time scheduling The melt-shop problem has also been solved using a continuous-time representation, which has the benefit that it offers an exact time representation to the scheduling problem and theoretically finds a global optimal solution. The main challenge is that in most standard continuoustime scheduling models, posing energy or material balances is non-trivial due to the lack of balancing points. One of the earlier approaches to tackle this is the approach by Ierapetritou and Floudas (1998), which was recently also implemented on a reduced melt-shop scheduling problem (Li et al., 2012). Here, unit-specific event points can be used for the energy balances in a natural way. In another approach by Nolde and Morari (2010), binary variables are introduced to track into which electricity grid-point each batch is assigned to. Similar idea was further adopted by Hadera and Harjunkoski (2013), where a part of the melt-shop model in Harjunkoski and Grossmann (2001) is refined with the same idea, taking into account both the electricity price as well as violations from a committed load curve. The benefit is that the energy considerations can be added on top of the original scheduling model by connecting the continuous start-time variables with new case-specific binary variables, according to Figure 17. This results in feasible solutions with clear energy savings potential but the approach is also in its pure form only applicable into smaller problem instances. Further contributions on the continuous-time approach within the steel domain is given by Haït and, Artigues (2011a), where an RTN-approach is adopted and Haït and Artigues (2011b) combining MILP with CP to overcome the computational challenge. Another problem in general is how to balance the usual objective function component (make span) with the

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Figure 16: RTN representation for a melt-shop process (Sun et al., 2013)

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The above difference must be taken into account in the formulation, for the details of which we refer to the cited publications. To consider the above listed differences mostly makes the model larger (variables and constraints) and more difficult to solve. In all of the cases it becomes clear that it is possible only to solve a limited size of the problem (reduced amount of batches, more coarse time grid or shorter time horizon) without some simplification approaches as discussed in Castro et al. (2013), where the melt-shop problem is represented in three different ways.

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To conclude, even if the discrete-time RTN approach enables easy incorporation of the energy constraints and provides a tight formulation it is difficult to solve problems where the time representation must be more exact. Some type of iterative schemes, rolling horizon approach or decomposition methods can be used for speeding up the solution of larger problems, which naturally compromises the global optimality.

Figure 17: Electricity price time slots (Hadera and Harjunkoski, 2013)

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4.7. Energy optimization of continuous production Castro et al. (2009), Mitra et. al. (2012) and others look into scheduling solutions for power intensive continuous processes from an academic perspective. Besides the academic world there are also existing industry proven solutions applying the Economic Flow Network (EFN) to minimize the energy cost. 4.7.1. Introduction to the Economic Flow Network (EFN) A tool for creating and modeling EFNs has been created by ABB. The Economic Flow Network model as shown in Figure 18 can be used to optimize production related costs and rewards in a complex industrial framework. An industrial site typically purchases, consumes, transfers, stores and sells energy in various forms. Typical forms of energy include electricity, gas, steam and other substances, as well as, motion and deformation of raw materials. Industrial sites are often interconnected and connected to other energy sources and energy users. The connections are made through the

electrical grid, gas pipelines etc. Besides energy, also material is transferred through an industrial site. Material is processed using energy and also stored e.g. in warehouses and tanks. The EFN can be used to create and maintain a model of all these relations. The interactions of different areas form a directed network consisting of nodes receiving, transmitting and storing energy, as well as, arcs presenting the capabilities and transfer functions of the routes between the nodes. The model is versatile yet simple to understand and can be configured to represent energy systems from single boilers to large multi-site industrial entities. The main benefit comes from the usage of the model together with real time energy and production data and forecasts of near future. In the EFNtool, this data is constantly fed into an optimization engine to get the best possible performance out of the targeted entities. Process dynamics can also be taken into account in a simplified way. Maximum and minimum run times as well as constraints for up and down ramping rates and on minimum and maximum load of the different processes and assets can be defined. The EFN optimizes the energy system by balancing each configured node (balancing area), while taking care of the restrictions in the interconnections and inside each node. This means that all inputs to a certain node must match with the outputs from the node, as shown in Eq. 6. A more comprehensive formulation is described in Harjunkoski et al. (2012).

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energy costs in order to get the best solutions. The problem size mainly grows along the number of heats and the number of time slots needed for the scheduling. If only the electricity price is considered and e.g. day and night tariffs exist, the number of electricity time slots may be very few but for load tracking it may be necessary to have a grid density of 60 or even 15 minute. In the latter case even medium-size problems become quickly intractable and – again – iteration of decomposition methods are needed to balance between optimality and efficiency.

Figure 18: The optimization problem for an energy system is defined as a flow network.

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There are several aspects to consider when running an automated continuous optimization scheme with varying input data, e.g. it is a requirement to solve the optimization tasks within the given time frame. To limit the problem size and thus the solution time, the time-horizon is split into several overlapping time slots using a Moving Horizon Estimation (MHE) approach. It is also crucial to avoid situations where the solution time might occasionally increase considerably above the average, causing sub-optimal plans or slow reactions to changes. To minimize the risk of excessive solution times, the solver can be divided into several competing instances with different parameterizations. In this approach, each solver uses different problem solving strategies and can thus be particularly well suited for a set of optimization scenarios. Together the solver family will be more likely to manage all upcoming tasks in reasonable execution time. 4.7.2. Use case: Thermo-mechanical pulping As an illustrative example, a thermo-mechanical pulping (TMP) line is a major energy user that can greatly benefit from proper energy management. In pulp and paper production there are several energy intensive areas. In the pulping process fibers are separated from wood chips to create pulp suitable for further processing. There are two main ways to produce pulp from wood chips; chemical or mechanical pulping. In chemical pulping a sodium or sulphur based cooking liquor dissolves the wood fibers in a so-called digester. Although energy management in chemical pulp mills may also be of interest (see e.g. Sarimveis et al, 2003), we focus here on the most electricity consuming pulp making process, i.e. mechanical pulping. In a TMP refiner huge amounts of electrical energy are used to separate the fibers from lignin and to make the fibers soft and useful for papermaking. In all high consistency (HC) refiners one or two large electric motors of typically 10-30 MW drive one or two rotors in the refiner such that all water not bound in the wood fibers is converted to steam inside the refiner. This steam is collected and used in the drying section of paper machines, which makes the paper mill integrated also from the thermal energy perspective.

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The flow network consists of flows (f) from a source (s) to node bj (first term), flows incoming from other nodes (second term), flows leaving the node bj to another node (third term) and finally flows from a node to the sink (t) (fourth term). The source and sink represent external energy import and export areas. The flow variables f are continuous and can be limited by various constraints. They exist for each time point considered. The target is to minimize the total cost of the flows. More formally, the entire system is modeled as a generalized minimum cost network flow optimization problem. Network optimization problems are extensively covered by Bertsekas (1991), Ahuja et al. (1993) and Bertsimas et al. (1997). The optimization of the model is carried out using a state-ofthe-art MILP solver. The introduction of integer (binary) variables into the model allows switching on/off some processing units, storage facilitates etc. As production related entities are dynamically started and shut down, their individual ramp-up and ramp-down characteristics need also to be considered in the optimization model. The EFN tool also enables custom parameterization of ramp-up and ramp-down sequences. As the energy system is dynamic, optimal energy plans and operational schedules are constantly updated as a reaction to changes in the real operating environment. During each optimization cycle, the optimal allocations and schedules are calculated for several days ahead. As new on-line measurement data becomes available, the deviations from the forecasted values are immediately reacted upon. This leads to system stability with ability to react on abnormal events like equipment failures. The system can also automatically adapt to changing conditions without user interaction. As a side-effect from automated decision making, the energy plan stays consistent and free of human errors. It is evident, that the size of the optimization problem increases together with the time span of the model. To get an idea of real-world problem sizes, see Table 1. Model sizes 1-4 in Table 1 are examples of large scale industrial optimization problems. As can be seen, the required number of constraints, variables and binary decision variables varies greatly from case to case. In all example cases the model size can be considered large. Sometimes, as in example model 2, no discrete decision variables are needed, which makes solving the optimization task easier – or computationally less demanding. For models with a vast number of discrete decision variables, the model size raises issues on the computational processing power needed to solve the problem in due time. In order to tackle the practical challenges of continuously solving large optimization problems several innovative steps need to be taken.

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Figure 19: The operational schedule of TMP lines is being optimized by the EFN tool based on actual and forecasted electricity prices.

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A rough estimate of the electricity need is 2500 kWh per produced ton of pulp, which is mainly consumed by the motors operating the grinders. The annual electricity consumption of a 300,000 ton TMP pulp mill is around 750 GWh, which translates to somewhere between 30 and 50 MEUR, if bought from the electricity markets. If the TMP plant of an integrated pulp and paper mill has excess capacity compared to the production in the paper machines, there is scope to schedule the pulp production with respect to volatile electricity prices, in order to minimize the total energy cost. The schedule of a TMP line can be optimized to minimize the energy costs and to maximize the profits from excess energy, by among others optimizing the use of intermediate storage capacity in accordance with the forecasted energy prices and planned pulp production. This scheduling has to be done taking into account a number of constraints, such as: total steam demand capacity and cost of alternative steam sources future production plan of the paper machine(s) the maximum production of each refiner line the minimum feasible production per refiner line Figure 19 shows a schedule for three TMP lines connected to one storage tank. The pulp consumption is forecasted from the paper and board production schedules for each pulp grade. With up-to-date consumption and energy cost forecasts, the pulp production plan can be optimized. within the respective requirements. The pulp production plan in turn creates forecasts for the pulp, process water, chemical, electricity, and steam balances

for the entire production process. This forecasted data can be used to schedule the energy intensive TMP operations and to control the intermediate storage levels to avoid high energy demand operations during peak energy prices and to respectively fill up reserves during more affordable hours. Integrated power production, commitments on energy market operations and changing energy prices can cause high variations to the hour-to-hour production costs of pulp, especially if last-minute changes in the pulp production schedule are accepted. Accurate day-ahead forecasts of consumption and production values of different energy resources enable profitable energy operations in the open energy markets. Late changes in paper and board production schedules or unplanned events e.g. sheet breaks increase the pressure for late changes in pulp production plans and deviations from forecasted energy balances. This problem was studied in Pulkkinen and Ritala (2008), where it was pointed out that some robust (stochastic) optimization is needed to not over-utilize the information in the forecasted electricity prices. Our implementation does not (yet) use robust optimization, but makes two other advances compared to Pulkkinen and Ritala (2008). Firstly the steam demand in the paper machines and the cost of alternative steam producers are explicitly taken into account. Secondly, instead of considering a TMP line as on/off, a feasible production interval is allowed, and below a specified minimum production volume the line is stopped.

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Figure 20: Energy flows in integrated steel mill

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4.7.3. Use case: Integrated steel mill by-product gas network optimization In a similar manner as in the TMP-case above, an integrated steel mill can greatly reduce the total energy bill by optimizing the use of internal gas and steam flows and storages, resulting in reduced cost of purchased energy and avoiding of the unnecessary flaring of excess gases. Iron and steel making processes with blast furnaces generate large volumes of energy rich (700 – 4300 KCal/Nm3) by-product gases. These gases can be consumed in the process, or used for generating steam and electrical power through a co-generation power plant. The key decision is to determine the optimum operating mode for all the energy system components at each moment, given the dynamic changes in the production processes and in the real time pricing of external energy markets. EFN can be readily applied to model this complex dynamic system (Figure 20), and to optimize the operations while meeting the gas and heat demands of all the consumers. The model takes as inputs the various fuel generation profiles, demand profiles of gas electricity and steam consumers, and the current state of the processes. The outputs from the model include the: supply of fuels to production sections and boilers boiler states and steam generation rates steam turbine states and electricity generation rates

amounts of electricity and gas to be purchased from external markets levels for the gas holder tanks excess amounts to be flared Based on real life data from 8 MTPA steel plant, the annual savings of using the integrated EFN model versus traditional way of operation have been estimated to be up to 12% of the total energy costs (electricity and gas purchase), corresponding to 2-5 MUSD annual savings.

5. Conclusions Reacting to rapid changes in operating conditions creates a need for short-term energy planning and optimization. In order to meet the needs and harvest all the potential underlying in the energy system, planning processes from production to energy management must operate seamlessly integrated on top of real-time data. Furthermore, an energy management system must be integrated with production management and mill control systems. This enables the forecasts and optimizations to be performed with up-to-date operative data. Since the daily operations are always subject to changes, the deviations from the ideal or planned schedule should be closely incorporated in the optimization cycle. The more advised the energy management is from these deviations the better the overall energy system can be adjusted to maximize profits in the current conditions.

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References

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Adekola, O., Stamp, J. D., Majozi, T., Garg, A. & Bandyopadhyay, S. (2013). Unified approach for the optimization of energy and water in multipurpose batch plants using a flexible scheduling framework. Industrial and Engineering Chemistry Research, 52(25), pp. 8488-8506. Ahuja, R.K., Magnanti, T.L. and Orlin, J.B. (1993) NetworkFlows: Theory, Algorithms and Applications. Prentice-Hall, Upper Saddle River, New Jersey, USA. Baboli, P.T; Moghaddam, M.P; Eghbal, M. (2011): Present status and future trends in enabling demand response programs. In : Power and Energy Society General Meeting, 2011 IEEE, pp. 1–6. Barbosa-Póvoa, A.P.F.D., Pinto, T., Novais, A.Q. (2001). Optimal design of heat-integrated multipurpose batch facilities: A mixed-integer mathematical formulation, Computers and Chemical Engineering, 25, pp. 547-559 Bertsekas, D. P., 1991, Linear Network Optimization, M.I.T. Press, Cambridge, MA. Bertsimas, D., Tsitsiklis, J. N., 1997, Introduction to Linear Optimization, Athena Scientific, Belmont, Massachusetts.Castro, P., Barbosa-Póvoa, A.P.F.D & Matos, H. (2001). An improved RTN continuous time formulation for the short-term scheduling of multipurpose batch plants. Industrial and Engineering Chemistry Research, 40, pp. 2059-2068 Castro P.M., Harjunkoski I. and Grossmann, I.E. (2009). New continuous-time scheduling formulation for continuous plants under variable electricity cost, Industrial and Engineering Chemistry Research, 48/14, pp. 6701-6714 Castro, P.M., Harjunkoski, I., Grossmann, I.E.(2011). Optimal scheduling of continuous plants with energy constraints Computers and Chemical Engineering, 35, pp. 372-387 Castro, P.M., Sun, L. and Harjunkoski, I. (2013). Industrial and Engineering Chemistry Research, 52, pp. 13046−13058 Charles River Associates (2005): Primer on Demand-Side Management. With an emphasis on price-responsive programs. Prepared for The World Bank. Chu, Y. and You, F. (2012). Integration of scheduling and control with online closed-loop implementation: Fast computational strategy and large-scale global optimization algorithm. Computers and Chemical Engineering, 47, pp. 248-268 Conti, John; Holtberg, Paul (2011): International Energy Outlook 2011. US Energy Information Administration. Demand Response Research Center (2013). Demand Response Research Center. From http://drrc.lbl.gov/ Diwekar, U., Shastri, Y. (2011). Design for environment: A stateof-the-art review, Clean Technologies and Environmental Policy, 13 (2), pp. 227-240 Engell S. and Harjunkoski I. (2012), Optimal Operation: Scheduling, Advanced Control and their Integration, Computers and Chemical Engineering, 47, pp. 121-133 European Union (2011): Energy 2020. A strategy for competitive, sustainable and secure energy. Fernández, I. , Renedo, C.J. , Pérez, S.F. , Ortiz, A. , Mañana, M. (2012). A review: Energy recovery in batch processes,

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Coping with changes within the energy supply structures Growing problem sizes through integration of problems Alignment to current industrial practice with distinct solutions Conflicting or competing operational targets in integrated solutions Proper balancing of targets, e.g. make span and energy costs Continuous-time methods: Efficient approach to formulate balancing points Discrete-time methods: Lacking accuracy

There are several approaches for full integration of production planning and energy management, which work well for academic size problems or specific industry use cases. An extension to more generic large industrial-size problems is still within the world of research, which also calls for a close collaboration with the industry.

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The complexity and the dynamics of the whole energy system require advanced computer aided planning and optimization capabilities to fully reach the available potential. Also, well-designed user interfaces are needed for communicating all the relevant aspects of the energy solution to all participants. As the limitations and capabilities affecting an energy system are subject to change, the models must be easily maintainable and understandable to reflect any changes in the operating environment. Some of the identified challenges are:

In order to meet the challenges, there are of course several opportunities within reach, for instance:

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Enterprise-wide optimization (EWO) Increased computer power Better optimization algorithms More flexible SW-infrastructure, providing easier access to data Standards, such as in energy management enabling easier collaboration Emerging methods and recent improvements in traditional scheduling technologies Cross-discipline concepts such as EFN and MPC

In this paper we presented several options on how EWO concepts can integrate energy management and scheduling. A better coordination of different scheduling solutions can reduce buffers between production steps, which can lead to significant energy savings. Solutions like EFN have shown that given the planned production schedule they are able to optimize the use of available energy sources and buffers and to schedule the flows of external energy and trading of excess energy to the markets for real world industrial problems. They can also be used to schedule process operations to avoid power peaks and to sell out excess energy with maximal profit. Lower power demand charges can be reached and additional bonuses can be gathered on markets incorporating demand side management benefits. A proper matching of the supply and demand side using internal buffers and production load shifting provides good opportunities to annual energy related costs savings of 2 to 5 percent.

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