STRUCTese® – Energy efficiency management for the process industry

Chemical Engineering and Processing 67 (2013) 99–110

Contents lists available at SciVerse ScienceDirect

Chemical Engineering and Processing: Process Intensification journal homepage: www.elsevier.com/locate/cep

STRUCTese® – Energy efficiency management for the process industry C. Drumm a,∗ , J. Busch b , W. Dietrich a , J. Eickmans b , A. Jupke b a b

Bayer Technology Services GmbH, 51368 Leverkusen, Germany Bayer MaterialScience AG, 51368 Leverkusen, Germany

a r t i c l e

i n f o

Article history: Received 2 April 2012 Received in revised form 1 September 2012 Accepted 18 September 2012 Available online 26 September 2012 Keywords: Energy efficiency Energy management system STRUCTese® Hybrid process

a b s t r a c t Besides the development of future energy concepts energy efficiency today offers a powerful and costeffective tool for achieving a sustainable energy future. Successful reduction of energy consumption and emissions requires a systematic approach. Thereby, energy management systems help to successfully reduce the energy consumption and CO2 e emissions. In the present work, a Structured Efficiency System for Energy (STRUCTese® ) was developed, which allows the detailed measurement and tracking of energy efficiency in contrast to measuring mere energy consumption. STRUCTese® provides full transparency about the status quo and the further improvement potential up to the limit of best available technology. Results for a real plant are presented which show the benefit of such a transparent visualization of energy efficiency. The work shows how the difficult task to measure and improve energy efficiency in a chemical plant can be mastered. © 2012 Elsevier B.V. All rights reserved.

1. Introduction The chemical industry is an energy-intensive industry. In 2009, the European chemical industry, including pharmaceuticals, used a total of 50.4 million tons of oil equivalent (TOE) of fuel and power consumption [1]. Energy therefore represents a significant share of operating expenses at chemical plants. The energy prices, and by this the percentage of energy on the total costs, have risen dramatically over the last 20 years. The prices for crude oil, which is on the one hand needed for energy generation but on the other hand also an important raw material, have risen from 20$/barrel in 1990 to over 100$/barrel in 2011. Reducing energy costs therefore becomes a key lever to decrease the operating and manufacturing costs and to increase profitability. Besides the cost argument, the climate change aspect becomes more and more important. The ICCA (International Council of Chemical Associations) report evaluates the chemical industry’s total production-related greenhouse gas (GHG) emissions at 2.1 GtCO2 e in 2005, a significant fraction of the total global anthropogenic emissions. Two-thirds of the production-related emissions come from energy use, over 5% of global energy-related CO2 e emissions [2,3]. As a large contributor to greenhouse gas emissions, the chemical industry committed itself to reduce these emissions and to take environmental and social responsibility. In this connection, the EU chemicals industry, including pharmaceuticals, has constantly and significantly reduced its fuel and power consumption

∗ Corresponding author. Tel.: +49 214 30 41978. E-mail address: [email protected] (C. Drumm). 0255-2701/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.cep.2012.09.009

from 1990 to 2009. The amount of energy consumed in 2009 was 27% less than in 1990, according to the European Commission data. Since the chemical production was increasing by 60% since 1990, the energy intensity, or energy consumption per unit of production, was 54% lower than in 1990 [1]. Energy efficiency is presently the most effective and economic lever to sustainably lower energy consumption. Increased energy efficiency can reduce investments into the energy infrastructure, minimize energy cost, and increase competitiveness. The reduction of greenhouse gas emissions and local pollution presents ecological advantages. Many chemical companies have been focused on reducing energy consumption for several years. Most often several discrete energy efficiency programs were carried out aiming to identify and implement measures for energy reduction. The key drawback of these programs is the fact that individual energy savings initiatives cannot sustain a high awareness level over time, leading to an “awareness gap” as shown in Fig. 1. The total savings of these single initiatives fall short of the potential at maximum implementation speed due to a slower step by step implementation. Note that the best possible energy consumption is also decreasing in the figure due to technological innovations. Over time, these companies lose performance as lost potential of the past cannot be compensated for. Therefore, a holistic approach and further steps in this direction are necessary. The raising awareness for climate protection also leads to an increasing interest of governments in energy efficiency and increasing regulation. By 2020, the German government aims to reduce Germany’s CO2 e emissions by 40% based on 1990. For this to succeed, legal instruments were implemented that will lead to more efficient use of energy. Several standards which require

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Fig. 1. Closing the awareness gap is a challenge.

Fig. 2. Simple monitoring of energy consumption over time.

2. Structured Efficiency System for Energy (STRUCTese® ) organizations to implement energy management systems (EnMS) were established. EnMS help to increase energy efficiency in companies and organizations. Among other things, an EnMS systematically records the energy consumption and serves as a basis mainly for investments in improving energy efficiency [4]. It is a tool that keeps track of measures that are planned and implemented to ensure minimum energy consumption for the current activity to enable continuous and systematic use of added energy saving potentials [4]. The DIN EN 16001 standard which came into force in July 2009 defines standardized EU-wide criteria for EnMS [4]. The new ISO 50001 is the first worldwide standard for EnMS published in 2011. Both ISO 50001 and EN 16001 follow a PDCA-cycle (Plan-Do-Check-Act) for continual improvement of the EnMS system. This includes the development of a policy for more efficient use of energy, target and objective setting and the usage of energy data to understand the energy consumption and to make decisions. As part of the PDCA-cycle the results are measured and the effectiveness of the policy must be reviewed to continually improve the energy management [5]. According to the EN 16001 the main reasons for the introduction of an EnMS are cost reduction, environmental protection, sustainable management, improvement of public image, use of financial incentives, and projection of climate policies. The introduction of an EnMS is particularly important for energy-intensive industries as the chemical industry. In order to benefit from tax reliefs in Germany it is necessary to have an EnMS according to ISO 50001 in place from 2013 on.

1.1. This work For the above reasons, Bayer positioned itself as a pioneer in climate protection, started a global climate program in 2007 and set ambitious targets for cutting greenhouse gas emissions. In 2008, Bayer MaterialScience, which has the highest energy consumption of the Bayer sub-groups, committed to reduce the specific greenhouse gas emissions (CO2 e per ton of product) by 25% in the period 2005–2020. In order to achieve these targets, the EnMS STRUCTese® (“Structured Efficiency System for Energy”) was developed as one of several lighthouse projects. The theoretical basics and the methodology of the EnMS, which is generally applicable to chemical companies, are presented in detail in the following paper. The paper also includes a case study on a polymer raw material plant, which served as the initial pilot plant for STRUCTese® . Till the end of 2011, the EnMS was implemented for 45 of the most energyintensive plants of Bayer MaterialScience. The practical experiences by using a pragmatic and holistic EnMS are presented in the paper. The paper is structured as follows: first the theoretical basics and the methodology are described in Section 2. Results and discussions for the case study are given in Section 3. Discussion and an outlook are presented in Section 4. At the end of the paper, a summary and conclusions are given.

Energy and CO2 e reduction programs aim at the reduction of global warming potential as well as the reduction of energy costs. To be successful and sustainable they require a systematic approach. In this chapter we will give an overview of the procedure we recommend. 2.1. STRUCTese® – managing energy efficiency The standards ISO 50001 and EN 16001 specify some important requirements for management systems such as the setting of targets and objectives and the measurement of energy consumption. Other important criteria for EnMS are the identification of the significant energy consumers and Energy Influencing Variables, the establishment of an energy baseline and the definition of energy performance indicators to monitor and measure energy performance. While it is rather easy to fulfill the basic requirements set by these standards, it is much more challenging to actually achieve excellence in energy efficiency in a large-scale organization. The available EnMS (see [6] for available German EnMS) on the market usually measure the total or specific energy consumption. However, the mere measurement or monitoring of total or specific energy consumption over time leaves open questions. Fig. 2 shows the specific energy consumption over time for two exemplary plants. From the development over time the cause of the fluctuations cannot be explained since the underlying influences and boundary conditions are not obvious. If the exemplary plants have different processes or produce different products, the questions arise where energy is used more efficiently or how energy efficiency can be compared across processes and products. For an ambitious target setting, the questions “Where is the limit of energy consumption for the specific plant?” or “How far down can we go?” must be answered. These questions are addressed by STRUCTese® allowing the direct measurement, tracking, benchmarking, and target-setting of energy efficiency in contrast to measuring energy consumption alone. The energy management cycle of STRUCTese® , which is in accordance with the described PDCA-cycle for EnMS, is depicted in Fig. 3. The method creates transparency by measuring energy efficiency, comparing energy efficiency across different products and plants and defining maximum energy efficiency by simulating ideal plant circumstances. Improvement ideas are fostered, generated and implemented on this basis. The energy development is continuously tracked to guarantee sustainable improvement. Finally, aspirational but realistic reduction targets are set for the next time period (e.g. year) to assure a continuous focus on energy efficiency and to guarantee a sustainable improvement. The systematic STRUCTese® approach is divided in two major parts, the Energy Efficiency Check (EE Check) and the Energy Efficiency Management (see Fig. 4). The STRUCTese® roll out within a unit starts with an Energy Efficiency Check (EE Check) and an Improvement Plan

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energy mix of a plant or a site, which consists of different energy utilities, i.e. electricity, steam, gas, and others, into values that are comparable between different production units and processes. Typically, the following energy categories will be considered: • Steam at different pressure levels • Electricity (distinguishing different voltage levels where appropriate) • Compressed air • Cooling water • Chilled water or brine • Special energies like ammonia, compressed nitrogen and others

Fig. 3. Energy Management Cycle: STRUCTese® follows a PDCA-cycle in accordance with the standards for EnMS.

based on identified improvement measures. The EE Check, consisting of analysis, idea generation and evaluation of energy efficiency potentials (see Fig. 4) is described in Section 2.3. In the next phase, the key element of STRUCTese® , the Energy Loss Cascade, which is presented in Section 2.4, is generated and implemented. This tool enables the comparison of efficiencies across different products and processes throughout the company and tracking of improvements over time. Current performance as well as technological, technical, and operational potentials at each plant are calculated and reported monthly. Details on how these potentials are identified and calculated can be found in Sections 2.5 and 2.6. Finally, as part of STRUCTese® an Online Monitor and a Daily Energy Protocol tool are introduced for permanent awareness of energy efficiency and for enabling immediate corrective actions (see Section 2.7). In doing so, STRUCTese® helps individual production plants to identify energy efficiency opportunities, provides specific guidelines to achieve these, and enables the plant to continuously measure and monitor energy efficiency in real time. Key Performance Indicators support the upper management in the annual target setting and review process (see Section 2.7). 2.2. Energy conversion to primary energy The energy scope of STRUCTese® , which takes into account energy production, distribution, and consumption within a production site, is depicted in Fig. 5. The energy scope is relevant for the conversion to primary energy and for the calculation of the static part of the cascade (see Section 2.5). In STRUCTese® , the energy demand as well as energy efficiency losses are measured individually for all processes in scope and converted into specific consumption of primary energy (PE) demand. The related measurement unit is kilowatt-hours primary energy per ton of produced product (kWhPE /t product). Primary energy is the energy contained in fossil fuels (natural gas, oil), which is transformed in energy conversion processes into more convenient forms of energy, such as electrical energy or steam [7]. This approach converts the actual

The specific fuel mix differs for each form of energy utility and also depends on energy utility production parameters at a particular site. In order to convert the secondary energy demand into primary energy demand, universal conversion factors based on global best practice conversion processes are employed. The usage of ideal factors means that the management system is not based on local or averaged energy mixes, but “world-best” demonstrated practices. For steam generation, e.g. this means that the very ambitious efficiency coefficient of 95% is used instead of the industry standard of 90% for a steam drum. The world-mix based efficiency for the production of electricity is 40%, whereas STRUCTese® follows the concept of benchmarking in-plant electricity generation with the best known technical projection for electricity generation worldwide, which is currently at 60% efficiency. This efficiency can currently only be achieved by gas turbine processes with cogeneration of steam and electricity [8]. 2.3. Energy Efficiency Check An Energy Efficiency Check (EE Check) is usually carried out as phase one of the STRUCTese® implementation. At Bayer it is part of the Bayer Climate Check, which combines the evaluation of energy efficiency potentials (EE Check) with the specific CO2 e emissions of industrial products (converting the various greenhouse gas emissions into comparable CO2 e) in a Climate Footprint. Details of the Climate Footprint, which is based on a Life Cycle Analysis, can be found elsewhere [9]. The EE Check identifies all potentials for energy savings in chemical plants and buildings. The workflow of an EE Check consists of 3 steps (see Fig. 4). It starts with a systematic analysis of overall energy consumption and energy distribution of a plant. A collection of relevant data and an analysis of overall utility consumption as well as an analysis of operating and equipment data with a focus on energy consumption and energy-relevant parameters are carried out. The goal is to create a complete database with total energy consumption and energy costs of all relevant energy consumers and producers. The results are used as a basis for optimization suggestions and in particular as the reference case to calculate potential savings of proposed improvement measures in the evaluation phase. Typical results of the analysis phase such as the energy distribution and the main energy consumers of a plant are depicted

Fig. 4. STRUCTese® workflow: Energy Efficiency Check and Energy Efficiency Management.

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Fig. 5. STRUCTese® energy scope.

Fig. 6. Typical results of the analysis phase – energy distribution of the plant (left) & main steam consumers (right).

in Fig. 6. For the main consumers local measurements of the energy consumptions (e.g. steam consumption for a distillation column or electricity consumption for a compressor) are desirable. Alternatively, their energy consumption can be estimated using mass and energy balances or installed motor power for an electricity consumer. In the second step, improvement ideas are collected by means of equipment checklists, best practices, input of process experts and a brainstorming session together with the plant staff. The goal of the idea generation is to determine all possible measures for energy reduction from different sources. Improvement measures range from simple operational adjustments to complex changes in the process structure. The full range of optimization levels including energy & utility supply, raw materials, heat integration, equipment, operational improvements, process design improvements, and buildings & facility is shown in Fig. 7. All improvement ideas are evaluated with respect to technical feasibility and profitability and sorted into 3 categories: • A: feasible and profitable (e.g. proven technology, no obvious concerns) • B: likely feasible and profitable, needs further evaluation (e.g. plant tests, more detailed investigation needed to determine feasibility or profitability) • C: not feasible or not profitable (technically not feasible C1, technically feasible but currently not profitable C2, and neither feasible nor profitable C3)

For technically feasible suggestions the savings potentials will be evaluated (amount of energy costs and primary energy savings, see Eqs. (1) and (2)), and a rough cost and profitability estimation is done for suggestions that require capital investment. Within the scope of the EE Check, energy costs savings potentials are quantified as the ratio of achievable savings and current total costs. Note that the energy costs typically consist of fixed costs and variable costs, where the latter of which scale linearly with the consumption. For

Fig. 7. Optimization levels considered in the Energy Efficiency Check.

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example, a two-part tariff is commonly used for electricity, in which the charge to the customer is divided in a demand rate (fixed, e.g. price per maximum demand in kW) and the energy rate (price per kWh). For the savings potentials, only a reduction of the variable costs is considered: Savings potential of energy costs [%] =

 variable costs variable costs + fixed costs (1)

Fossil energy use was identified as the most important driver of environmental burden [10]. The primary energy savings potential is therefore an important Key Performance Indicator for the ecological evaluation of an improvement measure. It is calculated from the primary energy savings as a ratio of total primary energy demand in the reference year. Savings potential primary energy [%] =

 primary energy primary energy

(2)

All ideas and potential projects are prioritized for implementation by means of an energy savings portfolio as shown in Fig. 8. The portfolio diagram gives a quick overview over the savings potential (from the bubble size), the technical feasibility and the profitability of the projects. Finally, the total savings potential of the EE Check in terms of costs and primary energy is calculated from all A- and B-projects that are not mutually exclusive using Eqs. (1) and (2). The top prioritized projects (e.g. the top 5 projects) are entered into the so-called Improvement Plan. For these projects, the schedule, e.g. the time for budget decision and detailed planning, the start of the construction work, the duration of the construction, and the planned project completion are defined. Thereby the plant management is supported by the Improvement Plan but also obliged to implement the most profitable energy savings measures. 2.4. Energy Loss Cascade The Energy Loss Cascade, which is the key element of STRUCTese® , is generated and implemented in phase two of the EnMS (see Fig. 4). The Energy Loss Cascade provides the plant managers with an easily accessible reporting tool to show the progress in energy reduction measures independently of the production rate. In addition, this tool allows diving into strategic options in terms of energy optimized plant structures and precise target setting due to a transparent methodology. Since the energy consumption is calculated as the specific consumption of primary energy even a comparison beyond similar processes is possible. Beyond simply measuring energy consumption, the Energy Loss Cascade allows the quantification and tracking of energy efficiency. A simplified cascade is depicted in Fig. 9. Different energy levels (CEC, OEO, PEO & TEO) describe the specific energy consumption of the plant under certain circumstances, while the difference between these energy levels is put and explained into several loss categories. The Energy Loss Cascade is divided into a static part, which is only dependent on the asset configuration (from OEO to TEO, see Section 2.5) and a dynamic part, which is changing over time, depending on the operation of the actual plant (from CEC to OEO, see Section 2.6). The Energy Levels in the static part, i.e. OEO and PEO do not change unless an investment project is realized, whereas the dynamic part of the cascade is changing due to different product types, quality requirements, production loads, operational conditions, and energy awareness. The energy levels are specific values and therefore the energy consumption (in kWhPE ) needs to be divided by the production (t) in the relevant period. For the static part of the cascade, the maximum theoretical capacity (MTC), the capacity which may be reached under ideal conditions, is applied.

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The Current Energy Consumption of the plant (CEC) shows the actual energy consumption of the plant (Fig. 9). The energy consumption over time as shown in Fig. 2 can easily be derived from this energy level. For the calculation of the CEC all energies passing the plant boundaries have to be taken into account. This includes energies entering the plant, which enlarge the CEC, as well as energies which are exported to an external customer, which reduce the CEC. The Operational Energy Optimum (OEO) represents the minimum energy consumption of the investigated plant, assuming its current design but optimal operation and zero operational losses. The Plant Energy Optimum (PEO) in the center of the static cascade represents the minimum specific energy consumption to produce the desired product at the given site and available infrastructure. The PEO is an important benchmark, which sets the ultimate energy efficiency target for the existing plant. Many measures that have to be considered in the PEO plant can be taken from the portfolio diagram (see Fig. 8) resulting from the EE Check, but also additional projects (see Section 2.5) or ideas (including revamping of complete process sections) can be taken into account. Finally, the Theoretical Energy Optimum (TEO) represents the specific energy consumption for the best known process and the best infrastructure. It is the limit beyond which lowering the specific energy consumption is not deemed possible based on today’s knowledge. TEO is a real value and is therefore higher than the Theoretical Energy Optimum derived from thermodynamics. There is only one TEO value worldwide for each process and utility. The loss categories which are in between the energy levels allow visualizing different sources of energy efficiency losses and identify the key levers for energy efficiency improvement. This approach assures transparency in the management of energy efficiency. Several loss categories are distinguished in the dynamic part of the cascade, visualizing efficiency losses due to operational aspects such as partial load or suboptimal operation (Fig. 9). Chemical plants usually have the best efficiency at maximum throughput, while the specific energy consumption is higher at partial load. In the static part of the cascade the loss code “process and infrastructure” shows energy losses due to production utilizing not the best practice chemical (and/or physical) process with best in class technology and optimum infrastructure. The loss category “suboptimal equipment” comprises all investment projects identified in the EE Check. These projects will appear ranked according to their category (A, B, C measures) to point out where improvements can be achieved most effectively. Even the projects which are currently not profitable or not yet proven technically feasible will appear in the Energy Cascade in order to maintain continuous awareness, as they might become profitable or technically feasible in the future. Once the circumstances change they can be reevaluated and moved to category A or B. The cascade and the different loss categories are calculated by an automatic system usually on a monthly basis. Experiences show that a monthly loss cascade, where the daily losses are aggregated up to monthly losses is the best procedure to filter temporary fluctuations and to provide an adequate time interval for a reporting tool. However, any desired time interval (daily, weekly) is possible. The automatic computation of the cascade needs the measured energy data for the dynamic part of the cascade (see Section 2.6). In general for every utility used in the production process an energy cascade can be set up. Nevertheless, the definition and calculation of energy cascades for utilities with neglectable share on the total energy consumption would result in unjustifiable effort. Therefore, cut-off criteria are used to maximize the benefit in contrast to the effort. Usually only energies with a contribution greater than 10% regarding energy costs, CO2 e contribution or energy consumption (in kWhPE ) are automatically in scope, whereas the aggregated cascade for a plant should represent at least 80% of the total energy consumption. Experiences have shown that these cut-off

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Fig. 8. Typical energy savings portfolio.

criteria are pragmatic and practical without losing necessary information. The Energy Loss Cascade for single utilities can easily be aggregated to the overall cascade by summing up the specific energy consumption for each energy level and loss code.

2.5. Static part of the cascade The different energy levels xEO (OEO, PEO, TEO) have to be defined and calculated following unambiguous rules. The xEO should be calculated with the “bottom-up” approach using process simulation models including mass and energy balances of the entire process. If the “bottom-up” approach cannot be used because the appropriate models are not available a “top-down” approach can be used as a compromise, e.g. calculating the PEO by means of the available OEO and the calculated loss code “suboptimal equipment”. Before the calculation of the xEO the models have to be validated to ensure that the representation of the plant energy balance is correct. Prior to the definition of the xEO, the relevant Energy Influencing Variables (EIV) have to be identified. Relevant EIV are key operating parameters which energetically control one or more relevant subsystems of the plant, for example pressure or reflux ratio in a distillation column. Ideal values are assigned to these EIV which correspond to the state of the art with respect to the corresponding technology or have been challenged and proven at least in pilot

plant scale. Recipe parameters have to correspond to the worldwide Best Demonstrated Practice (BDP). The OEO is calculated applying the energetically optimal operational parameters for the current plant design. There are examples where minimizing energy consumption and maximizing product yield are conflicting goals. One example is the reflux ratio of distillation columns, where a higher ratio may reduce the loss of product but also increase the energy demand. Same can account for a chemical reaction if the energetically optimal recipe differs from the economical optimum. Neither high amounts of educt or product losses nor infinite energy demand is reasonable, so that a compromise must be found. STRUCTese® resolves the issue along the following line of thought: the CO2 footprint of lost educts/products is in general by far greater than the CO2 equivalents of the related energy consumption in the process. Hence, educt or product losses in order to save energy is not useful in the long run. Ambitious operational specifications (e.g. an accepted degree of educt or product losses) must be defined as boundary conditions for the energetically optimal operation. The basic structure for the PEO plant is the same as for the existing plant. PEO includes all parts of the plant which fall into the responsibility of the plant management. Facilities for energy generation which are located outside of the plant are out of scope (outside battery limit, OSBL). In contrast, energy generation within the control of the plant is included in the PEO (inside battery limit, ISBL). For the transition from the OEO to the PEO investment projects

Fig. 9. Energy Loss Cascade.

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Fig. 10. Concept of Best Demonstrated Practice, consideration of suboptimal operation and partial load.

are considered. The existing chemical process and infrastructure at the specific site is fixed, therefore the redesign of the plant or subunits must be possible keeping the existing structure of the plant. The PEO plant is based on the state of the art. Consequently all the measures considered need to be proven in at least pilot plant scale. The EE Check results in a detailed list of measures for energy savings which must be considered in the design of the PEO plant. By default, subunits and equipment are redesigned to reduce the energy consumption further. The STRUCTese® methodology contains a number of well-defined guidelines for cutoff criteria, where too ambitious equipment designs are not plausible from a technical or economical point of view, as e.g. heat exchangers with an infinite heat exchanger area. An example for such a guideline are evaporators which are replaced by multi-stage evaporators with heat integration. The number of theoretical separation stages is increased one by one as long as the energy consumption is lowered by more than 2% for each stage. An optimal energy integration concept is developed and analyzed in detail using Pinch Analysis [11,12] with respect to the boundaries for energy integration due to technical feasibility and safety reasons. The PEO finally represents the minimum specific energy consumption which can be reached if the plant is refurbished independently of profitability or return on investment. For obtaining the TEO all available information such as literature and patents should be used. Processes are only accepted as TEO if they are published in peer reviewed scientific literature or demonstrated at least in lab scale. TEO is based on the chemical process with the lowest specific energy consumption known worldwide and assumes the ideal infrastructure for a given process. Hence, OSBL which are related to strategic decisions by the responsible site management are now also in scope. Process sections or even the whole process might be changed from PEO to TEO. 2.6. Dynamic part of the cascade In the dynamic part of the cascade, the gap between the current (CEC) and the minimum energy consumption (OEO) of the existing plant is assigned to several loss codes (e.g. suboptimal operation and partial load). This is done by statistical data analysis of available process data. It is often possible to derive BDP values for the relevant energy consumers from available process data. Daily or hourly averages of specific energy consumption (for all utilities or single utilities for all relevant consumers) are plotted against the production capacity as shown in Fig. 10. The BDP curve is defined as the interconnection of the best operating points (after outlier removal) and represents the minimum specific energy consumption which

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was achieved in the past. Specifically, the BDP curve allows to separate the dominant influence (e.g. load) from “other” influences (e.g. suboptimal operational parameters). The energy consumption for every day or hour (one dot in the diagram) is compared to the BDP curve, pinpointing the real losses due to suboptimal operational parameters. The deviation of the BDP curve at the actual load to the BDP at MTC shows the influence of partial load, which cannot easily be optimized by the plant personnel but can sometimes be improved e.g. by process control loops. Furthermore, a simple modeling procedure is carried out to model the energy consumptions (e.g. steam) as a function of the EIV (e.g. load, fouling, product quality, ambient temperatures, pressures). This is partly done via simple rigorous approaches (if available) or via linear regression. Ordinary or partial least squares regression of available process data is carried out to identify linear models for the relevant energy consumptions as a function of the EIV [13]. If adequate linear relationships exist, many more loss categories in the dynamic cascade can be subtracted from the loss code suboptimal operation. For example, if the influence of fouling, catalyst aging or ambient temperature can be determined, the plant data can be idealized by removing the influence of these EIV. These losses, which cannot directly be influenced by the operators, can be subtracted from the losses due to suboptimal operation. In this approach, the quality of underlying models, which is reflected by the coefficient of determination (e.g. R2 > 0.8), is paramount for the robustness of the results.

2.7. Online Monitoring (OM), Daily Energy Protocol (DEP) and Key Performance Indicators (KPI) The Energy Loss Cascade is supplemented by further measures that create continuous awareness for energy efficiency on all levels of the organization. A real-time energy efficiency Online Monitor (OM) enables the plant operator to minimize avoidable energy losses by providing continuous feedback on Current Energy Consumption and energy savings potentials. The current operating point of the plant is compared with the BDP, i.e. the corresponding energetically optimal operating point which has been achieved in the past. The OM typically visualizes specific energy consumption and losses. In addition to this specific energy view, a cost view (e.g. Euros per day) can be provided. This latter view helps operators to better prioritize their efforts to reduce avoidable losses according to the respective cost savings. Along with the energy consumption and losses, the OM shows the EIV of the plant, thereby enabling operators to better understand the dependencies between energy consumption and key operational parameters. The OM requires measured data for all relevant energy consumptions and EIV from a suitable process information management system (PIMS). Typically, hourly averages are employed for all calculations and displays. It is recommended to implement the OM within the framework of an existing PIMS. A typical OM is shown in Fig. 11. Daily Energy Protocols (DEP) summarize average energy consumptions and energy relevant process parameters of the last 24 h for the discussion in the morning meeting of the plant team. It is based on the same input data as the OM and follows the same idea of comparing the current operating point with the BDP; however, it provides an aggregate view – i.e. daily averages or totals – of the relevant variables rather than continuous trends. Typically, the Daily Energy Protocol allows a direct comparison of several previous days and also shows the weekly average. The cascades themselves are not suitable to be compared in their absolute value of energy levels. In order to establish an energy efficiency benchmarking and allow for energy efficiency target setting, Key Performance Indicators (KPI) have to be defined. The KPI are

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Fig. 11. Exemplary energy efficiency Online Monitor – several energy levels, Best Demonstrated Practice and loss codes are shown for the main utilities.

calculated by means of the energy levels and the energy losses of the cascade. In general, the KPI can be described as: xE 2 =



xEO , energy losses+xEO

(3)

where xE2 reflects an energy efficiency (E2 ) of a specific cascade section by comparing an energy level (xEO = OEO, PEO, TEO) to the deviation from this energy level due to different energy losses. The lower the losses, the closer the KPI is to the theoretical target of 100%. Depending on the Energy Level and the losses considered, different KPI with different meanings are derived. Each KPI can be presented in its development over time and allows comparing across different plants, products, and processes. Especially the market and load corrected KPI mPE2 is suitable for target setting and benchmarking by not considering partial load influences (e.g. due to sales difficulties) mPE 2 =



PEO

suboptimal equipment+

dynamic losses−Partial Load+PEO

(4)

mPE2 could be tracked for a single plant over time to show the development in energy efficiency. Another possibility would be the comparison between different products (and processes) to identify the product (or process) with the biggest optimization potential. By means of these supplementing measures, STRUCTese® provides the foundation for a benchmarking and reporting tool and a communication platform between hierarchy levels as shown in Fig. 12. The system supports plant personnel to optimize the operation of their units, plant management to focus on the most effective measures, and general management to develop an overall efficiency strategy, set targets, and track progress. In doing so, the system is embedded in all hierarchy levels creating acceptance in contrast to top-down-only approaches. With this integrated approach, the whole company is oriented towards energy efficiency from the upper management to the plant operator. 2.8. STRUCTese® pushes hybrid and integrated processes Since STRUCTese® focuses on the energy optimum and the lowest possible energy consumption, new and innovative (separation)

processes must be considered. Distillation processes, which account for a large percentage of the energy consumption in chemical processes, oftentimes exhibit a low energy efficiency [14]. In contrast, hybrid or integrated separation processes can lead to reductions up to 70% of the energy consumption. One possible approach to improve efficiency is to combine existing distillation operations with more efficient separation processes such as membrane or adsorption separators [15]. For example, the potential benefits of hybrid distillation–pervaporation processes on the energy efficiency are widely investigated [16–20]. Many different combinations are feasible, for example hybrid processes combining reactive distillation and membranes [21–23], distillation, permeation and adsorption [24] or crystallization, extraction and distillation [25]. The design and optimization of a hybrid process is a challenging task because of many degrees of freedom involved [25]. No standardized methods for their optimum layout are available today. There is an urgent need for methods which estimate energy optimum separation processes and their energy consumption and consider new and innovative processes and equipment. On the one hand, by defining these processes as a benchmark and goal in the cascade, STRUCTese® can push the development of new separation methods. On the other hand, STRUCTese® needs these new innovative methods for the definition of an ambitious energy minimum. Therefore, EnMS and particularly STRUCTese® and hybrid processes benefit from each other. 3. Case study The results of the pilot project where STRUCTese® was first implemented at a real chemical plant are presented in this chapter. The plant produces a polymer raw material with an energy consumption of >200 GWh per year. 3.1. EE Check Before 2007, only single energy savings projects were carried out. The EE Check was carried out in 2007 and 2008. Initially, a detailed analysis of the present energy consumption was accomplished, in order to identify the largest saving potentials. Fig. 13

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Fig. 12. Integrated energy efficiency management tool addressing all levels of the organization.

shows the fraction of the different utilites of the overall energy consumption. The focus of the EE Check was then placed on steam and electricity consumers since these utilities account for 99% of the total energy consumption. As part of the analysis, a steam balance of the plant was set up and a pinch analysis was carried out. For the consumers of electricity, the motor list and the control concept of each pump was studied. As outcome of the EE Check an extensive list with 46 suggestions for the reduction of the energy consumption was compiled. The evaluation of the individual measures as described in Section 2.3 resulted in 11 measures being classified as technical feasible and profitable or likely feasible and profitable (category A and B). Several “quick wins” were identified, i.e. measures which can immediately be implemented without investments. On the basis of the implementation of the quick wins alone approximately 4% of energy was saved. Most of these measures were implemented directly or latest by the end of 2008. For example, it was found that the reflux ratio of a distillation column could be reduced to a significantly lower level despite varying loads. In a second process modification, an increase of the condensate temperature from 60 ◦ C to 70 ◦ C was implemented, which could save steam used for heating a cycle stream. Both measures were implemented without noteworthy investment. Other measures which necessitate a prior investment resulted in a savings potential of approximately 7%. These measures comprise better heat integration (e.g. preheat of distillation column feeds with bottom streams, usage of enthalpy of condensation) and better insulation. The total saving potential was estimated as 60,000 tCO2 e/a.

3.2. Energy Loss Cascade In the next step, the Energy Loss Cascade was set up in 2009. According to the cut-off criteria, only Energy Loss Cascades for steam and electricity were calculated. For the conversion of electricity to primary energy the STRUCTese® conversion factor of 1.66 kWhPE /kWh electricity was applied. The energy content of steam entering the plant is defined by the energy which is needed to produce steam in the power plant, where the boiler feed temperature, the superheating of the steam and the boiler efficiency is also taken into account. For 16 bar steam 820 kWhPE /t steam was calculated. For the static part of the cascade, the xEO energy levels OEO down to TEO were defined. First of all, the xEO values for the EIV and the xEO energy levels were defined by the project team. The transition from the OEO to the PEO comprises different heat integration measures, packing as new column internals, preheater at all columns, and a few new pumps. Main differences for the TEO plant were a different reaction process, dry operated vacuum pumps, and a condensate recycle to the power plant. The final xEO plants were simulated by means of a steady-state process model. For the dynamic part of the cascade, a statistical data analysis was carried out applying the concept of BDP to identify the influence of partial load on the energy consumption. The identified BDP curve over load for steam is depicted in Fig. 14. As expected, the operation at partial load results in much higher specific energy consumption. The minimum energy consumption is found at maximum load. By means of the BDP curve, it was possible to extract the influence of partial load from the overall energy consumption to identify the losses due to suboptimal operation.

Fig. 13. Case study energy consumption.

Fig. 14. BDP curve data analysis for steam.

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Fig. 15. Energy Loss Cascade 2011 for all energies (case study).

The Energy Loss Cascade for steam and electricity for the year 2011 is shown in Fig. 15. Note that the plant also exports energy to other plants (300 kWhPE /t), which is already included in the cascade resulting in a lower specific energy consumption. The cascade discriminates between losses due to partial load, which cannot be reduced without better process control and investment, and losses due to suboptimal operation, which can be improved by the plant personnel. Besides suboptimal operation and partial load a further loss code “planned operation” was introduced, where intentional, process related deviations from running the plant at the energy optimum are considered. In the static part of the cascade, the improvement measures with investments are included in the loss code “suboptimal equipment”. The transparency generated by the energy cascade is very useful if energy consumption is compared over several months as shown in Fig. 16 for the steam consumption. While the total steam consumption is fluctuating over time, it becomes obvious that these fluctuations are a result of partial load operation, while the losses due to suboptimal operation are constantly decreasing. This approach therefore creates transparency in the management of energy efficiency and avoids wrong conclusions. While the cascades and energy losses on a monthly or daily basis show the short term benefit of the transparent visualization of energy efficiency, the long term benefit of STRUCTese® can be shown in the yearly cascades. The yearly Energy Loss Cascades for steam from 2006 to 2011 are depicted in Fig. 17. The Energy Loss Cascade shows the energy levels from PEO to CEC. The TEO for steam was calculated as 331 kWhPE /t using the TEO process model and was constant over the years. Over 6 years, the CEC was lowered by 26% (1441 → 1059 kWhPE /t). It is visible that the partial load was also decreasing, thus, the plant was operating closer to the MTC

in recent years. The real success becomes visible in the loss code “suboptimal operation”. These energy losses, which can be influenced by the plant team by more energy-efficient operation, were reduced by 50% (447 → 232 kWhPE /t). The energy efficient operation was supported by the installation of an OM, which visualized

Fig. 16. Development energy efficiency over time (case study).

Fig. 17. Steam Loss Cascade from 2006 to 2012 (case study).

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Fig. 18. Improvement of the Best Demonstrated Practice (case study).

the current energy efficiency, and the Daily Energy Protocol, which was discussed during morning meetings. Fig. 17 also shows that the OEO decreased. Two new heat exchangers were installed in 2009, resulting in a lower operational optimum. In addition, further measures were implemented in 2010. Additonal feasible measures were also identified for the PEO during a yearly revision of the cascade 2011 resulting in a lower value (406 instead of 508 kWhPE /t). A revision of the cascade in 2011 found that the Best Demonstrated Practice was also improving as shown in Fig. 18. The BDP curve was therefore adjusted to the lower values resulting first in higher overall losses due to suboptimal operation in 2010. These losses were again lowered to the previous value in 2011, representing further improvements in operation. The example of the pilot project impressively demonstrates the benefits of STRUCTese® as shown in Fig. 3. All energy levels (CEC, OEO and PEO) as well as the losses could be lowered over the years. The system gives constant support from the first idea of energy efficiency measures to their sustainable implementation. The constant focus on energy efficiency fosters the idea generation over years as shown in the present example. STRUCTese® accelerates the process of rapid and sustainable implementation of identified energy savings potentials, which is usually a significant challenge for various reasons. There is usually a lack of time for energy efficiency measures in the daily business, investments for plant modifications are not approved, and energy efficiency is only a secondary objective behind safety, yield, throughput and quality. In contrast, STRUCTese® raises awareness for energy efficiency on all levels of the organization by means of the OM, the Energy Loss Cascade, and the KPI and integrates it into the daily workflow. The lost potential if an investment in the static cascade is not realized is always visible. The fastest way to the PEO is known. The system creates transparency by visualizing the losses of energy efficiency and the focus remains on the achievable energy optimum. The workload for the implementation of STRUCTese® is manageable and small in comparison to the exceptional advantages. The implementation of STRUCTese® for a plant can be realized within 60–120 person days depending on the complexity of the plant. 4. Discussion and outlook As a result of the impressive results of the pilot project, the rollout of STRUCTese® at Bayer MaterialScience has commenced in 2009 and included already 45 plants at the end of 2011. The rollout will be completed by the end of 2013 and will comprise the 60 most energy-intensive plants. Up to now, a reduction of energy consumption by 585,000 MWh and annual CO2 e emissions by 175,000 t have been realized by implementing a first wave of the identified efficiency measures. Ultimately, Bayer MaterialScience expects to realize an annual CO2 e emission reduction of 700,000 t

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by implementing all efficiency measures in the 60 most energyintensive plants. Based on the encouraging results throughout the STRUCTese® roll-out so far, Bayer MaterialScience has just recently stretched its climate target from 25 to 40% reduction of specific CO2 e emissions until 2020 compared to the 2005 baseline. Although STRUCTese® is already well developed, there are still some needs for further improvements. STRUCTese® was designed for world-scale energy-intensive chemical production processes and is tailor-made to the needs of the chemical industry. In principle, rules are available to apply the method to batch processes and to production processes in the process industry in general. Since the method depicts the specific energy consumption per ton of product, problems can arise if many different products are manufactured on multiple parallel production lines. The aggregation to one Energy Loss Cascade is still possible but can be very complex in dynamic situations where the distribution of the products to the lines is changing or the lines are operated at different part load. For small or discontinuous production processes and for processes with lesser energy consumption, e.g. Life Science processes, the method is too complex and expensive. In addition, when no process models or continuous energy measurements are available careful reductions and adjustments of the methodology are necessary to maintain its benefits. So far, the optimum is defined by means of the results of the EE Check and xEO workshops where the achievable energy minimum is discussed and defined by process experts, who know their process and best available technology. In contrast, to establish a standard, an objective estimation of the energy optimum is desirable, which could be done by means of rigorous modelling and shortcut-methods provided that these are available for the considered processes. For these reasons, these isues are currently addressed in a public funded project, where the methodology is extended towards a universal management and benchmarking standard for energy efficiency suitable for any kind of production facility in the process industry [26].

5. Summary and conclusions The energy management system STRUCTese® (Structured Efficiency System for Energy) and its heart, the Energy Loss Cascade, were introduced. The idea of the Energy Loss Cascade is to compare the Current Energy Consumption (CEC) of a production plant to its specific Operational Energy Optimum (OEO), Plant Energy Optimum (PEO) and Theoretical Energy Optimum (TEO) and to break down and explain the differences between these energy levels in loss categories. STRUCTese® allows a detailed measurement and tracking of energy efficiency, which provides full transparency about the status quo and the further improvement potential up to the limit of the best available technology. Results for a real plant were presented which show the benefit of such a transparent visualization of energy efficiency. It was shown that the losses of the real plant due to suboptimal operation were lowered by 50% over 6 years, while the overall specific energy consumption was lowered by 26%. This was possible by continuous focus on energy efficiency at all hierarchical levels of the organization. While an Online Monitor and a Daily Energy Protocol supported the plant personnel to reduce the operational losses, the plant management was supported to reach the PEO step-bystep. During this time period, STRUCTese® provided support to implement improvement measures rapidly and sustainably, and to constantly reduce the Operational and Plant Energy Optimum by fostering and implementing new improvement measures. It was shown how the difficult task to measure and improve energy efficiency in a chemical plant and an entire organization can be mastered.

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The methodology meets the described requirements of the ISO 50001 standard for energy management systems and was certified according to the EN 16001 in 2010. STRUCTese® provides a vision and a sustainable development plan towards an energy efficient future. Acknowledgement We would like to acknowledge the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung) for financial support (BMBF project number: 01 RC1008A/B). References [1] Cefic European Facts & Figures Energy Report 2011, European Chemical Industry Council, Brussels, 2011. [2] Innovations for Greenhouse Gas Reductions, International Council of Chemical Associations, Arlington, 2009. [3] R.S. Kandel, Turning the Tide on Climate Change: The Climate Change Challenge and the Chemical Industry, Cefic, Brussels, 2009. [4] W. Kahlenborn, S. Kabisch, J. Klein, I. Richter, S. Schürmann, DIN EN 16001: Energy Management Systems in Practice, Federal Ministry for the Environment, Nature Conservation and Nuclear Safety, Berlin, 2010. [5] International Organization for Standardization, Win the energy challenge with ISO 50001, International Organization for Standardization, Geneva, 2011. [6] EnergieAgentur.NRW, EMS.marktspiegel für Energiemanagement-Software, www.energieagentur.nrw.de/emsmarktspiegel, 2011. [7] A. Kydes, Primary energy, in: C. Cleveland (Ed.), Encyclopedia of Earth, Environmental Information Coalition, National Council for Science and the Environment, Washington, 2012. [8] R. Kehlhofer, B. Rukes, F. Hannemann, Combined-Cycle Gas & Steam Turbine Power Plants, Pennwell Corp., Tulsa, 2009. [9] M. Wolf, B. Himmelreich, J. Korte, Analysis methods for CO2 balances, in: H.-J. Leimkühler (Ed.), Managing CO2 Emissions in the Chemical Industry, WileyVCH, Weinheim, 2010. [10] M. Huijbregts, S. Hellweg, R. Frischknecht, H. Hendriks, K. Hungerbühler, A. Hendriks, Cumulative energy demand as predictor for the environmental burden of commodity production, Environmental Science & Technology 44 (2010) 2189–2196. [11] B. Linnhoff, Pinch analysis, Chemical Engineering Progress 33 (1994). [12] M. Ebrahim, A. Kawari, Pinch technology: an efficient tool for chemical-plant energy and capital-cost saving, Applied Energy 65 (2000) 45–49. [13] R. Rao, H. Toutenburg, Shalabh, C. Heumann, Linear Models and Generalizations – Least Squares and Alternatives, Springer, Berlin, 2007. [14] T. Andersen, G. Siragusa, B. Andresen, P. Salamon, S. Jørgensen, Energy efficient distillation by optimal distribution of heating and cooling requirements, in: Proceedings of the 10th European Symposium on Computer Aided Process Engineering, Computer Aided Chemical Engineering 8 (2000) 709–714. [15] M. Sorin, E. Ayotte-Sauvé, Y. Azoumah, F. Rheault, On energy efficiency of hybrid separation processes, Chemical Engineering Transactions 12 (2007) 67–72. [16] P. Kreis, A. Górak, Process analysis of hybrid separation processes: combination of distillation and pervaporation, Chemical Engineering Research and Design 84 (2006) 595–600.

[17] J. Haelssig, A. Tremblay, J. Thibault, A new hybrid membrane separation process for enhanced ethanol recovery: process description and numerical studies, Chemical Engineering Science 68 (2012) 492–505. [18] F. Lipnizki, R. Field, P.-K. Ten, Pervaporation-based hybrid process: a review of process design, applications and economics, Journal of Membrane Science 153 (1999) 183–210. [19] M. Del Pozo Gomez, J.-U. Repke, D. Kim, D. Yang, G. Wozny, Reduction of energy consumption in the process industry using a heat-integrated hybrid distillation pervaporation process, Industrial and Engineering Chemistry Research 48 (2009) 4484–4494. [20] Z. Szitkai, Z. Lelkes, E. Rev, Z. Fonyo, Optimization of hybrid ethanol dehydration systems, Chemical Engineering and Processing 41 (2002) 631–646. [21] C. Buchaly, P. Kreis, A. Górak, Hybrid Separation processes – Combination of Reactive Distillation with Membrane Separation, Chemical Engineering and Processing 46 (2007) 790–799. [22] J. Holtbrügge, P. Lutze, A. Górak, Modeling, simulation and experimental investigation of a reactive hybrid process for the production of dimethyl carbonate, in: Proceedings of the 11th International Symposium on Process Systems Engineering, Computer Aided Chemical Engineering 31 (2012) 1241–1245. [23] S. Steinigeweg, J. Gmehling, Transesterification processes by combination of reactive distillation and pervaporation, Chemical Engineering and Processing 43 (2004) 447–456. [24] T. Roth, P. Kreis, Rate based modelling and simulation studies of hybrid processes consisting of distillation, vapour permeation and adsorption for the dehydration of ethanol, in: Proceedings of the 19th European Symposium on Computer Aided Process Engineering, Computer Aided Chemical Engineering 26 (2009) 815–819. [25] M. Franke, N. Nowotny, E. Ndocko Ndocko, A. Górak, J. Strube, Design and optimization of a hybrid distillation/melt crystallization process, AIChE Journal 54 (2008) 2925–2942. [26] C. Drumm, Energy Efficiency Management – Energieeffizienz-Management und – Benchmarking für die Prozessindustrie, http://www.chemieundco2.de/ media/10 EE Management.pdf, 2011.

Glossary BDP: Best Demonstrated Practice CEC: Current Energy Consumption DEP: Daily Energy Protocol EE Check: Energy Efficiency Check EIV: Energy Influencing Variables EnMS: energy management system ISBL: inside battery limit KPI: Key Performance Indicators MTC: maximum theoretical capacity OSBL: outside battery limit OEO: Operational Energy Optimum OM: Online Monitor PDCA: Plan-Do-Check-Act cycle PE: primary energy PEO: Plant Energy Optimum PIMS: process information management system STRUCTese® : Structured Efficiency System for Energy TEO: Theoretical Energy Optimum xEO: OEO, PEO or TEO