Integrated Energy Efficiency in Production

9th Modelling, Management and Available online at www.sciencedirect.com 9th IFAC IFAC Conference Conference on on Manufacturing Manufacturing Modelling, Management and 9th IFAC Conference on Manufacturing Modelling, Management and Control 9th IFAC Conference on Manufacturing Modelling, Management and Control 9th IFAC Conference on Manufacturing Modelling, Management and Control Berlin, Germany, August 28-30, 2019 Control Berlin, Germany, August 28-30, 2019 ControlGermany, August 28-30, 2019 Berlin, Berlin, Germany, August 28-30, 2019 Berlin, Germany, August 28-30, 2019

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IFAC PapersOnLine 52-13 (2019) 135–140

Integrated Energy Efficiency in Production Integrated Energy in Integrated EnergyB.Efficiency Efficiency in Production Production T. Heutmann*, Zhou*, C. Schommer*, Integrated EnergyB. in Production T. Heutmann*, Heutmann*, B.Efficiency Zhou*, C. C. Schommer*, Schommer*, T. Zhou*,

T. B. C. R. H. Schmitt** R. H. Schmitt** T. Heutmann*, Heutmann*, B. Zhou*, Zhou*, C. Schommer*, Schommer*, R. H. Schmitt** R. H. Schmitt**  R. H. Schmitt**  *Fraunhofer Institute IPT, Aachen, Aachen, Germany, Germany,  Technology *Fraunhofer Institute for for Production Production Technology IPT, *Fraunhofer Institute for Production Technology IPT, Aachen, Germany, *Fraunhofer Institute for Production Technology IPT, Aachen, (Tel: 0049-241-8904-245; e-mail: [email protected]). (Tel: 0049-241-8904-245; e-mail: [email protected]). *Fraunhofer Institute for Production Technology IPT, Aachen, Germany, Germany, (Tel: 0049-241-8904-245; e-mail: [email protected]). 0049-241-8904-245; e-mail: [email protected]). ** Laboratory Laboratory(Tel: for Machine Machine Tools and and Production Production Engineering (WZL), (WZL), RWTH RWTH Aachen Aachen University, University, ** for Tools Engineering (Tel: 0049-241-8904-245; e-mail: [email protected]). ** Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, ** Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen Aachen, Germany, (e-mail: [email protected]) Aachen, [email protected]) ** Laboratory for Machine Tools and (e-mail: Production Engineering (WZL), RWTH Aachen University, University, Aachen, Germany, Germany, (e-mail: [email protected]) Aachen, Germany, (e-mail: [email protected]) Aachen, Germany, (e-mail: [email protected]) Abstract: Due to to an increasing energy energy price price and and progressively progressively emphasized emphasized environmental environmental politics, politics, an an Abstract: Due an increasing Abstract: Due to an increasing energy price and progressively emphasized environmental politics, an Abstract: Due to an increasing energy price and progressively emphasized environmental politics, an energy-efficient production has obtained significant attention in the past years. Intensive research has been energy-efficient has obtained significant attention in the past years. Intensive research has been Abstract: Due toproduction an increasing energy price and progressively emphasized environmental politics, an energy-efficient production has obtained significant attention in the past years. Intensive research has been energy-efficient production has obtained significant attention in the past years. Intensive research has been conducted on the two main energy consumers in production environment: production activities and conducted on the two main energy consumers in production environment: production activities and energy-efficient production has obtained significant attention in the past years. Intensive research has been conducted on the Some two main energy consumers in production environment: production activities and conducted on two energy consumers in production environment: production activities and building facilities. approaches consider two systems together for aa further energetic building facilities. approaches even consider combining two systems together for energetic conducted on the the Some two main main energyeven consumers incombining production environment: production activities and building facilities. Some approaches even consider combining two systems together for aa further further energetic building facilities. Some approaches even consider combining two systems together for further energetic potential. However, most researches remain in theoretical levels. Industrial implementations do not follow potential. However, most researches remain in theoretical levels. Industrial implementations do not follow building facilities. Some approaches even consider combining two systems together for a further energetic potential. However, most researches remain in theoretical levels. Industrial implementations do notfor follow potential. However, most researches remain in levels. implementations do follow the research results. This reviews the theoretical development of energy-efficiency measures both the research results. This paper reviews the theoretical development of energy-efficiency measures both potential. However, mostpaper researches remain in theoretical theoretical levels. Industrial Industrial implementations do not notfor follow the research results. This paper reviews the theoretical development of energy-efficiency measures for both the research results. This paper reviews the theoretical development of energy-efficiency measures for both systems and takes a close insight of the integrated energy saving researches. Afterwards a concept to systems and takes a close insight of the integrated energy saving researches. Afterwards a concept to the research results. This paper reviews the theoretical development of energy-efficiency measures for both systems and takes a close insight of the integrated energy saving researches. Afterwards a concept to systems and takes a close insight of the integrated energy saving researches. Afterwards a concept to integrate an energy-control loop into a Manufacturing Execution System (MES) is presented. In addition, integrate an energy-control loop into a Manufacturing Execution System (MES) is presented. In addition, systems and takes a close insight of the integrated energy saving researches. Afterwards a concept to integrate an energy-control loop into a Manufacturing Execution System (MES) is presented. In addition, an energy-control loop into a Manufacturing Execution System (MES) is presented. In addition, aintegrate second concept develops an energy-efficient building automation connected with a MES. They provide aintegrate second concept develops an energy-efficient building automation connected with a MES. They provide an energy-control loop into a Manufacturing Execution System (MES) is presented. In addition, second practicable concept develops develops an energy-efficient energy-efficient building automation connectedthis withpaper MES. They aprovide provide aaindustry second concept an building automation connected with aaa MES. They industry solutions with manageable adaption costs. Ultimately, outlines novel solutions with manageable adaption costs. Ultimately, outlines novel aindustry second practicable concept develops an energy-efficient building automation connectedthis withpaper MES. They aaprovide practicable solutions with manageable adaption costs. Ultimately, this paper outlines novel industry practicable solutions with manageable adaption costs. Ultimately, this paper outlines a novel solution approach to combine MES and building automation together in order to reach an overall-optimized solution approach to combine MES and building automation together in order to reach an overall-optimized industry practicable solutions with manageable adaption costs. Ultimately, this paper outlines a novel solution approach to combine MES and building automation together in order to reach an overall-optimized solution approach to combine MES and building automation together in order to reach an overall-optimized energy consumption. Copyright © 2019 IFAC energy consumption. Copyright © 2019 IFAC solution approach to combine MES and building automation together in order to reach an overall-optimized energy consumption. Copyright © 2019 IFAC energy consumption. Copyright © energy Copyright © 2019 2019ofIFAC IFAC © 2019,consumption. IFAC (International Federation Automatic Control) Hosting by Elsevier Ltd. planning All rights and reserved. Keywords: Energy efficiency, integration, modelling, predictive control, production control, Keywords: Energy efficiency, integration, modelling, predictive control, production planning and control, Keywords: Energy efficiency, integration, modelling, predictive control, production planning and control, Keywords: Energy efficiency, integration, modelling, predictive control, production planning and control, building automation building automation Keywords: Energy efficiency, integration, modelling, predictive control, production planning and control, building automation building automation building automation   

1. INTRODUCTION 1. INTRODUCTION 1. INTRODUCTION 1. INTRODUCTION 1. INTRODUCTION Due to an increasing energy price and growing threats of Due to an increasing energy price and growing threats of Due to an increasing increasing energy price price production and growing threats of Due to an energy and growing threats of global warming, an energy-efficient has obtained global warming, an energy-efficient production has obtained Due to an increasing energy price and growing threats of global warming, an energy-efficient production has obtained global warming, an energy-efficient production has obtained significant attention in the past years. European politics even significant attention in the past years. European politics even global warming, an energy-efficient has obtained significant attention in the past past with years.aproduction European politics even significant attention in the years. European politics even legislate the energy reduction defined target through legislate the energy reduction with a defined target through significant attention in the past years. European politics even legislate the energy reduction with a defined target through legislate the energy reduction with a defined target through publishing theenergy National Energywith Efficiency Action Plan (i.a. publishing the National Energy Efficiency Action Plan (i.a. legislate the reduction a defined target through publishing the Statistics National show Energy Efficiency Action Plan Plan (i.a. publishing the National Energy Efficiency Action (i.a. BMWi, 2017). that the manufacturing industry BMWi, 2017). Statistics show that the manufacturing industry publishing the National Energy Efficiency Action Plan (i.a. BMWi, 2017). Statistics show that the manufacturing industry BMWi, 2017). Statistics show that the manufacturing industry is the largest energy consumer in 2015, consuming is the largest energy consumer in 2015, consuming BMWi, 2017). Statistics show that the manufacturing industry is largest energy consumer in consuming is the the largest of energy consumer in 2015, 2015,equivalent consuming 3.92 petajoules energy per year in Germany, to 3.92 petajoules of energy per year in Germany, equivalent to is the largest energy consumer in 2015, consuming 3.92 petajoules of energy per year in Germany, equivalent to 3.92 petajoules of energy per year in Germany, equivalent to 39% of total energy consumption (UBA, 2018). Consequently, 39% of total energy consumption (UBA, 2018). Consequently, 3.92 petajoules of energy per year in Germany, equivalent to 39% of total energy consumption (UBA, 2018). Consequently, 39% of total energy consumption (UBA, 2018). Consequently, researchers and manufacturers seek for energy-efficient researchers and manufacturers seek for energy-efficient 39% of total energy consumption (UBA, 2018). Consequently, researchers manufacturers seek researchers and and manufacturers seek for for energy-efficient energy-efficient manufacturing to avoid cost pressure. manufacturing to avoid cost pressure. researchers and manufacturers seek for energy-efficient manufacturing manufacturing to to avoid avoid cost cost pressure. pressure. manufacturing to avoid cost pressure. Production activities, which apply diverse resources like Production activities, which apply diverse resources like Production activities, which apply diverse resources like Production activities, which apply diverse resources like material, equipment, and labour to generate outputs of material, equipment, and labour to generate outputs of Production activities, which apply diverse resources like material, equipment, and labour to generate outputs of material, equipment, and labour to generate outputs of performance, service, and goods, are one main energy performance, service, and goods, are one main energy material, equipment, and labour to generate outputs of performance, service, and goods, are one main energy performance, service, and goods, are one main energy consumer. The organization of such activities belongs to the consumer. The organization such activities to the performance, service, and of goods, are one belongs main energy consumer. The organization of activities belongs to the consumer. Theof organization of such such and activities belongs to the main function production planning control (PPC). Thus, main function of production planning and control (PPC). Thus, consumer. The organization of such activities belongs to the main function of production planning and control (PPC). Thus, main function of production planning and control (PPC). Thus, many researches focus to bring energetic aspects into PPCmany researches focus to bring energetic aspects into Thus, PPCmain function of production planning and control (PPC). many many researches researches focus focus to to bring bring energetic energetic aspects aspects into into PPCPPCsystems. systems. many researches focus to bring energetic aspects into PPCsystems. systems. systems. Building facilities, which provide necessary and comfortable Building facilities, which provide necessary and comfortable Building facilities, which provide necessary and comfortable Building facilities, which provide necessary and comfortable environments of production activities, such as heating, venting, environments of production activities, such as heating, venting, Building facilities, which provide necessary and comfortable environments of production activities, such as heating, venting, environments of production activities, such as heating, venting, and air-conditioning (HVAC), but also lights, blends as well well and air-conditioning (HVAC), but also lights, blends as environments of production activities, such as heating, venting, and air-conditioning (HVAC), but also lights, blends as well and air-conditioning (HVAC), but also lights, blends as well as safety controls are considered as the second main energy as safety controls are considered as the second main energy and air-conditioning (HVAC), but also lights, blends as well as safety controls are considered as the second main energy as safety controls are considered as the second main energy consumer within manufacturing. Building automation is in consumer within manufacturing. Building automation is in as safety controls are considered as the second main energy consumer within manufacturing. Building automation is in consumer within manufacturing. Building automation is in charge of the automatic centralized control of such facilities. charge of the automatic centralized control of such facilities. consumer within manufacturing. Building automation is in charge of the automatic centralized control of such facilities. charge of the automatic centralized control of such facilities. Therefore, advanced control theories have been developed in Therefore, advanced control theories have been developed in charge of the automatic centralized control of such facilities. Therefore, advanced control theories have been developed in Therefore, advanced control theories have been developed in this field to improve energy efficiency. this field to improve energy efficiency. Therefore, advanced control theories have been developed in this field to improve energy efficiency. this field to improve energy efficiency. this field to improve energy efficiency.

Both systems together consume 90 % of overall energy of aa Both systems together consume 90 % of overall energy of Both systems together consume 90 % of overall energy of a Both systems together consume 90 % of overall energy of manufacturing plant (BMWi, 2018). Building automation manufacturing plant (BMWi, automation Both systems together consume 2018). 90 % ofBuilding overall energy of aa manufacturing plant (BMWi, 2018). Building automation manufacturing plant (BMWi, illumination, 2018). Building automation delivers necessary temperature, and clean air for delivers necessary temperature, illumination, and clean air for manufacturing plant (BMWi, 2018). Building automation delivers necessary temperature, illumination, and clean air for delivers necessary temperature, illumination, and clean air for production operation, whilst production activities and production operation, whilst production activities and delivers necessary temperature, illumination, and clean air for production operation, whilst production activities and production operation, whilst production activities and corresponding resources influence the environment corresponding resources the environment production operation, whilstinfluence production activities and corresponding resources the environment correspondingTherefore, resources influence theof environment accordingly. Therefore, energyinfluence consumption of both both systems accordingly. energy consumption systems corresponding resources influence the environment accordingly. Therefore, energy of systems accordingly. Therefore, energy consumption consumption of both both consider systems are highly coupled. Nonetheless, most researches are highly coupled. Nonetheless, most researches consider accordingly. Therefore, energy consumption of both systems are highly coupled. Nonetheless, most researches consider are highly coupled. Nonetheless, most researches consider them isolated from each other. Energy-oriented PPC realizes them isolated from each other. Energy-oriented PPC realizes are highly coupled. Nonetheless, most researches consider them isolated from each other. Energy-oriented PPC realizes them isolated from each other. Energy-oriented PPC realizes improved job scheduling without considering additional improved job scheduling without considering additional them isolated from each other. Energy-oriented PPC realizes improved job scheduling without considering additional improved job scheduling without considering additional energy consumption of building facilities. Advanced building energy consumption of building facilities. Advanced building improved job scheduling without considering additional energy of building facilities. Advanced building energy consumption consumption ofenergy building facilities. of Advanced building automation optimizes consumption facilities without automation optimizes energy consumption of facilities without energy consumption of building facilities. Advanced building automation optimizes energy consumption of facilities without automation optimizes energy consumption of facilities without aaautomation proactive utilization of thermal effects from production. A proactive utilization of thermal effects from production. A optimizes energy consumption of facilities without amutual proactive utilization of thermal effects from production. A amutual proactive utilization of thermal effects from production. A communication between them and an optimum of total communication between them and an optimum of total amutual proactive utilization of thermal effects from production. A communication between them and an optimum of total mutual communication between them and an optimum of total energy efficiency cannotbetween be identified identified in current current research and energy efficiency cannot be in research and mutual communication them and an optimum of total energy cannot identified in research and energy efficiency efficiency cannot be be identified in current current researchboth and industry. Consequently, it is necessary to integrate industry. Consequently, it is necessary to integrate both energy efficiency cannot be identified in current research and industry. Consequently, it is necessary to integrate both industry. Consequently, it is necessary to integrate both systems and to develop a jointed control for a further systems and to developit aa isjointed control for a further industry. Consequently, necessary to integrate both systems to control for systems and and potential to develop develop a jointed jointed control for aaa further further optimization regarding energy consumption. optimization potential regarding energy consumption. systems and to develop a jointed control for further optimization optimization potential potential regarding regarding energy energy consumption. consumption. optimization potential regarding energy consumption. In this context, this paper reviews the theoretical development In this context, this paper reviews the theoretical development In this context, this paper reviews the theoretical development In this context, this paper reviews the theoretical development of energy efficiency measures for both systems as well as of energy efficiency measures for both systems as well as In this context, this paper reviews the theoretical development of energy efficiency measures for both systems as well as of energy efficiency measures for both systems as well as jointed controls approaches. Then, a concept to integrate an jointed controls approaches. Then, a concept to integrate an of energy efficiency measures for both systems as well as jointed controls approaches. Then, a concept to integrate an jointed controls approaches. Then, a concept to integrate an energy-control loop into a Manufacturing Execution System energy-control into aa Manufacturing Execution System jointed controlsloop approaches. Then, a concept to integrate an energy-control loop into Execution System energy-control loop In into a Manufacturing Manufacturing Execution System (MES) is presented. presented. In addition, a second second concept concept develops an (MES) is addition, a develops an energy-control loop into a Manufacturing Execution System (MES) In aa second concept develops an (MES) is is presented. presented. In addition, addition, second conceptwith develops an energy-efficient building automation connected aa MES. energy-efficient building automation connected with (MES) is presented. In addition, a second concept develops an energy-efficient building automation connected with aa MES. MES. energy-efficient building automation connected with MES. Lastly, this paper outlines a novel solution approach to Lastly, this paper outlines aa novel solution approach to energy-efficient building automation connected with a MES. Lastly, this paper outlines novel solution approach to Lastly, this paper outlines a novel solution approach to combine MES and building automation together to reach an combine MES and building automation together to reach an Lastly, this paper outlines a novel solution approach to combine MES and building automation together to reach an combine MES and building automation together to reach an overall-optimized energy consumption. overall-optimized consumption. combine MES andenergy building automation together to reach an overall-optimized overall-optimized energy energy consumption. consumption. overall-optimized energy consumption.

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2. ENERGY EFFICIENCY IN PRODUCTION

satisfying. Overshoots of controlled parameters are not avoided and implemented systems are unstable. Therefore, research focus has been switched to predictive or adaptive controllers. With a mathematical model, controllers could predict the thermal behavior of a building and avoid overheating of HAVC, which results in an improved energy consumption (Henze, 2004). Ma (2009) and Široký (2011) extent the mathematical model with weather forecasts to describe the thermal capacity of buildings in order to utilize the heating and cooling effects of atmosphere. Several experimental analyses in real buildings are conducted to verify the energy saving potential. Oldewurtel (2012) even succeeds to combine predictive controller with occupancy detection to achieve energy efficient control of HVAC, lighting, and blinds.

2.1 Production processes In the past decade, research results of energy-efficient PPC were very fruitful. Junge (2007) identifies the optimization of production processes and single machines by the use of an intelligent PPC with the most potential to increase energy efficiency. Different approaches focus on energy management for single machines (Abele, Eisele & Schrems, 2012; Eisele, Schrems & Abele, 2011; Kara & Li, 2011; Verl et al., 2011a; Wenzel et al., 2011). All of them predict energy consumption and formulate measures to reduce it. Models for the energy behaviour of machine tools lay the foundation for the control of complex process chains in production (Haag et al., 2012; Verl et al., 2011a). This enables the energy-optimized operation of such machines. Verl et al. (2011b) presents a model-based machine control for machine tools to increase energy-efficiency.

2.3 Overall energy efficiency Since 2008, researchers have recognized the potential to integrate both PPC and building-automation systems. Several approaches focus initially on modelling of the interaction of both systems in terms of their energy consumptions. Martin et al. (2008) deals with the development of an analytical tool by coupling various simulation software. As a result, the overall energy-load profiles can be calculated for different production scenarios. Haag et al. (2012) forecasts the energy consumption and evaluates it for various production planning scenarios. Brundage et al., (2014) consider the dynamical energy price structure into the simulation model and calculates the future energy requirements and costs. Based on the modelling theory, Sun et al. (2016) control successfully HVAC while keeping the room temperature constant, achieving a significant energy reduction. On the contrary, Khan et al. (2017) derives daily scheduling strategy to minimize HVAC energy costs. Furthermore, Sobottka et al. (2018) fill the simulation method with a combined mathematic model to simulate the thermal behavior of production operation and building facilities, which allows a more accurate calculation of energy demands.

Another focus of works addressing energy efficiency lies in the creation of energy-optimized production schedules. The concepts according to Junge (2007), Thiede (2011), Rager (2008), Khalaf (2012), Schultz, Sellmaier and Reinhart (2015) as well as Grosse Boeckmann (2014) are to be named here. A further approach is the planning of the entire production process regarding energy consumption and load peaks according to Pechmann and Schoeler (2011). Fang et al. (2011) extends the classic job-shop scheduling with aspects of energy planning. Furthermore, other concepts are energy-optimised planning and evaluation by dismantling and block formation of individual consumers (Weinert, Chiotellis & Seliger, 2011), the use of key figures (Bonneschky, 2002), and the development of suitable, practical evaluation systems (Krings, 2012). However, none of the approaches successfully integrates energy efficiency into a MES, implements it into a real production context, and connect it to building automation system.

Simply, modelling the energy consumption of both systems has been realized. The optimization of both systems has been studied for its feasibility. Yet, a practicable solution to attain the communication and coordination of both PPC and building automation cannot be identified.

2.2 Building automation In the past 20 years, advanced control of building automation has been well investigated. One direction of research is towards condition monitoring through sensors as well as extended data resources. A sensor-based modelling and prediction of user behaviour has been developed and connected to the building energy and comfort management systems (Dong, 2009). Another solution utilizes several motion detectors to identify room occupancy information for the lighting system control (Agarwal et al., 2010). Research project ProKlim integrated weather forecasts to optimize the blind control to save energy and improve the comfort level (Kahn et al., 2017).

3. CURRENT CONCEPTS In this section, two validated approaches focusing on production planning and building automation are presented. 3.1 Energy-oriented MES with project eMES The approach of a control loop focusing on energy-orientation in production control has been demonstrated and validated in industry successfully within the research project eMES (Heutmann & Schmitt 2017). It is based on the energy-control loop by Grosse Boeckmann (2014) but extends the concept by an Energy Control System (ECS) (Fig. 1). In addition to Production Data Acquisition (PDA), the ECS monitors energy consumption. Therefore, it is part of the sensor system to identify any disturbance effect. Consequently, a comprehensive monitoring of energy- and manufacturingrelated activates is achieved.

Another research direction lies on the development of controllers. Classical controllers such as rule-based or Proportional-Integrate-Derivative (PID) controllers are intensively studied and applied to minimize energy consumption. However, results from practice have not been 140

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The concept focuses on the purchase time and peak loads to minimize energy costs. Both are part of the contract signed with the grid operator and can be influenced by the production. Therefore, the concept integrates energy rates into the energyoriented scheduling. The resulting three utilization strategies are: minimizing peak loads, using off-peak periods, and being able to shed a load short-term.

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and the exchange of their order numbers. Due to the coding form, the admissibility of the solution candidate is maintained. For the validation of the algorithm, the following parameter have been used (see Table 2): Table 2. Parameterization of the genetic algorithm Parameter Population size Generations Tournament size of the selection Mutation probability

Within the MES, the energy-oriented schedule can be planned by the sorting rule First-in-First-out (FIFO) or by hand with drag-and-drop (D&D). An energy-oriented X-control chart has been implemented where the upper action limit is based on the first two utilization strategies. Furthermore, a relative deviation of 10 % of the planned load profile leads to actions. A relative deviation of 5 % defines the warning limit. Additionally, four intervention rules are formulated to identify deviations: run, trend, 10 % deviation from mean, and ≥2 exceedance of warning limits.

Value 250 200 2 0.1

The result of the validation within a software demonstrator is shown in Fig. 2. The optimization algorithm outnumbers the other two planning possibilities (FIFO and D&D) for all three categories: energy costs, downtime costs, and delay costs. 79 €

80 €

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8,000 €

4,000 € 3,481 €

Besides that, an optimization algorithm has been developed. The algorithm combines a genetic algorithm (e.g. Bierwirth & Mattfeld, 1999 or Thamilselvan & Balasubramanie, 2012) with a Giffler-Thompson-method (Giffler & Thompson, 1960) as the opening procedure. The target function TF combines energy procurement costs CEP, machine downtime costs CMD, and costs for delayed delivery dates CD. The elements are individually weighted 𝑤𝑤𝑥𝑥. TF is defined as follows:

Control

Correcting variables

Deviations from standard

As-IsComparison PDA/MDA

Downtime costs

Energy costs

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Algorithm

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0€

FIFO

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0€

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D&D

FIFO

Algorithm

D&D

FIFO

Algorithm

Fig. 2. Performance comparison of optimization algorithm with other planning strategies.

Based on this, the main algorithm is defined. Initially, the coding of solution candidates is achieved by the use of a permutation with repetition (Yang, 2010). It is selected since it can guarantee the admissibility of the coded solution with regard to the technological sequence, because only the order number is taken into account. The following selection process is done by a tournament selection due to the high efficiency (Goldberg & Deb, 1991; Razali & Geraghty, 2011; Shukla, Pandey & Mehrotra, 2015). The selection of an operator is normally linked to the coding. Considering this, the Generalization of Oder-Crossover by Bierwirth (1995) is applied. The strength of such an operator lies in the fact that in two permutations the relative positions of individual orders are very well transferred into the newly created solutions. Furthermore, a simple mutation is defined with a random selection of two elements of the respective solution candidate

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D&D

(2)

Reference Variable Delivery dates, Energy availability

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with ∑ 𝑤𝑤 = 𝑤𝑤 + 𝑤𝑤 + 𝑤𝑤 = 1. 𝑥𝑥 𝐸𝐸𝐸𝐸 𝑀𝑀𝑀𝑀 𝐷𝐷

Urgent orders, Request load shedding Production Process

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68 €

(1)

Disturbance values

3,500 €

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𝑇𝑇𝑇𝑇 = 𝑚𝑚𝑚𝑚𝑚𝑚(𝐶𝐶𝐸𝐸𝐸𝐸 × 𝑤𝑤𝐸𝐸𝐸𝐸 + 𝐶𝐶𝑀𝑀𝑀𝑀 × 𝑤𝑤𝑀𝑀𝑀𝑀 + 𝐶𝐶𝐷𝐷 × 𝑤𝑤𝐷𝐷 )

MES Production Scheduling

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77 €

Delay costs

78 €

3.2 Energy-efficient building automation with project BIG Within the research project BIG, the approach of an energyefficient building automation has been realised and validated in industry. Beyond the theoretical studies presented in section 2, BIG succeeds to develop a building automation software not only connected with MES, but also applying a predictive controller. This resulted in a further energy reduction of building facilities. The overall approach is illustrated in Fig. 3. Through a message broker, MES delivers operation plans to the building automation software. On the one hand, the plans of employee allocation and machine utilization can be interpreted as the accurate requirements of working temperature. As known, the setting temperatures of diverse production activities are different, not to mention the time without occupation. On the other hand, the collected future production activities can be calculated as thermal impacts to the indoor environment. Together with impacts from weather forecasts, an extended module of building is established and named as Predictor, which calculates and simulates the thermal behaviour of a production building. This facilitates the Weather Forecast

MES Employee/ machine plan

Control Variable Lead time, Adherence to delivery, Machine utilization, Load profile, Energy consumption

Air temperature/ sunshine

Predictor Reference Variables

Energy Controlling ECS MES Manufacturing Execution System MDA Machine Data Acquisition ECS Energy Controlling System PDA Production Data Acquisition

Room temperature, Humidity, Air quality

Fig. 1. Energy control loop adjusted for integration into a MES (based on Grosse Boeckmann, 2014).

Derivation

Actual Room Temperature

Predictive Controller

Building automation Control Variables On/Off plan of HAVC Lighting

Sensors

Production environment

Fig. 3. Control loop of energy-efficient building automation. 141

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basis of a Predictive Controller: the second extended module of this software. Based on calculated future heating or cooling demands, this controller looks for a combination of all possible HVAC settings with the minimum energy consumption.

(14 °C to 26 °C) should be maintained during time off work. Through the implemented message broker, the daily production planning was provided to the building automation software.

Simply, three significant strategies are applied to this unique energy-efficient building automation:

Table 3. Defined constraints of the Predictive Controller No.

 Identifying accurate temperature requirements,  Using waste heat from production, and  Searching for the most energy-efficient measures of HVAC. Predictor is mathematically based on the classical heat balance of the indoor air. Considering all possible thermal impacts to the indoor air, the development of room temperature 𝜃𝜃𝑖𝑖𝑖𝑖 is defined as follows: 𝜃𝜃𝑖𝑖𝑖𝑖 = ∫

1 2 3 4 5

1 𝑑𝑑𝑑𝑑𝑠𝑠𝑠𝑠𝑠𝑠 [𝜏𝜏𝑤𝑤 × + 𝑈𝑈𝑖𝑖𝑖𝑖 (𝜃𝜃𝑤𝑤𝑤𝑤 − 𝜃𝜃𝑖𝑖𝑖𝑖 ) + 𝑄𝑄̇𝑚𝑚𝑚𝑚 𝛿𝛿𝑖𝑖𝑖𝑖 𝜌𝜌𝑖𝑖𝑖𝑖 𝑐𝑐𝑖𝑖𝑖𝑖 𝑑𝑑𝑑𝑑 (3) + 𝑄𝑄̇𝑒𝑒𝑒𝑒 ] 𝑑𝑑𝑡𝑡

with δin = Thickness of indoor air ρin = Density of indoor air cin = Thermal capacity of indoor air τw = Transmissivity of windows θsun = Solar radiation Uin = Thermal transmittance of indoor air θwa = Temperature of wall surface Q̇ ma = Thermal energy from machines Q̇ em = Thermal radiation from employees.

The result of the validation of a typical production day (24 hours) is presented in Fig. 4. Compared with another two traditional control possibilities (PID and rule-based), a significant energy reduction can be observed. 450 €

Energy costs of HVAC

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PID Rule-based MPC controller controller controller

Here only two channels of thermal gaining from weather forecasts to buildings are considered: convection from atmosphere air temperature to the indoor air through walls and the solar gains through windows. Additionally, heat gains from production activities consist of waste heat from machines and thermal radiation of employees. The machine waste heat can be defined through measurement, while the thermal energy of each employee is assumed as 140 𝑊𝑊/𝑚𝑚. All those heat gains sum up to the final energy balance of the indoor air.

Fig. 4. Comparison of energy cost for different controllers. 4. INTEGRATED SOLUTION APPROACH As presented in chapter 3, extended functionalities and modules of MES and building automation systems provided industry possibility to adapt its organizational operation with manageable software costs so that a significant energy reduction of two major consumers in production can be realised. However, the overall energy efficiency has never been explored into such industrywide popular software but stagnates in theoretical studies. This does not fulfill the urgent demands from industry. Therefore, a new concept is introduced to address an optimization of both MES and building automation software simultaneously, based on the valuable experiences of eMES and BIG, aiming to further energy saving. Fig. 5 proposes the conceptual structure.

The Predictive Controller is based on the theory model predictive control (MPC). The configuration of this controller follows the main MPC principle, where control variables are managed by fitting a cost function and considering sets of constraints. The cost function CF aims to look for the minimum energy consumption of HVAC, therefore a simple linear function is chosen: 𝐶𝐶𝐶𝐶 = 𝑚𝑚𝑚𝑚𝑚𝑚(𝐸𝐸ℎ + 𝐸𝐸𝑣𝑣 + 𝐸𝐸𝑎𝑎𝑎𝑎 ) with Eh = Energy consumption of heating, Ev = Energy consumption of venting, Eac = Energy consumption of air-conditioning.

Constraints If heating on, then air-conditioning off; If air-conditioning on, then heating off If air-conditioning on, then venting off; If venting on, air-conditioning off Reference temperature -1 oC ≤ Room temperature ≤ Reference temperature +1 oC Output of heating ≤ Max. heating capacity Output air-conditioning ≤ Max. cooling capacity

(4)

Besides, constraints are defined to limit the ranges of control variables (Table 3). A real production scenario has been simulated in a MATLAB environment for the validation, where 8 milling machines constitute of a metal processing job shop within a cubic building. The product tolerance specifies a constant working temperature at 20 °C, while a big range of room temperature 142

This concept sticks to the principle of MPC as the linkage of both MES and building automation. All three thermal streams from production activities, atmosphere, and building facilities that influence the room temperature are fed to the Energy Predictor, so that the future energy consumption of the whole system can be simulated. According to the reference variables (delivery dates and room temperature) defined by MES, the Energy Controller provides the decisions of both MES and building automation simultaneously. This decision mechanism is multi-objective, because the conflict between on-time delivery of products and the energy consumptions happens frequently. Hence, a multidimensional cost function with

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different weighting is the core of this approach and must be introduced into the controller. Based on inputs of this extended control loop, both systems facilitate still a prediction of energy consumption, and consequently an improvement of energy efficiency. Conclusively, the resulting strategies are listed here:  An additional module for MES and building automation is established, which predicts and controls both of them.  A prediction is complemented compared with current studies through consideration of major thermal influencing factors inside of production.  A substantive improvement is the realization of simultaneous control and optimization of both systems.  The application of a control loop enables a real-time feedback and thus a real-time adjustment of both systems. Within this approach, production orders and weather forecasts as system inputs are provided, on time delivery of orders and total energy consumption are handled as objectives, when daily job schedules and operation plans of HVAC are the resulting output. This allows a constant communication of both MES and building automation to reach an overall optimum.

Abele, E., Eisele, C. and Schrems, S. (2012). Simulation of the energy consumption of machine tools for a specific production task. In Dornfeld D., Linke B. (ed.), Leveraging Technology for a Sustainable World, 233-237. Springer, Berlin, Heidelberg. Agarwal, Y., Balaji, B., Gupta, R., Lyles, J., Wei, M., & Weng, T. (2010). Occupancy-driven energy management for smart building automation. Proceedings of the 2nd ACM workshop on embedded sensing systems for energyefficiency in building, 1-6. Bierwirth, C. (1995). A generalized permutation approach to job shop scheduling with genetic algorithms. In OR Spektrum, 17 (2-3), 87–92. Bierwirth, C. and Mattfeld, D.C. (1999). Production scheduling and rescheduling with genetic algorithms. In Evolutionary Computation, 7 (1), 1–17. BMWi − Federal Ministry for Economic Affairs and Energy (2017). National energy efficiency action plan (NEEAP) 2017 for the Federal Republic of Germany. Federal Ministry for Economic Affairs and Energy, Berlin. BMWi − Federal Ministry for Economic Affairs and Energy (2018). Energieeffizienz in Zahlen. Federal Ministry for Economic Affairs and Energy, Berlin. Brundage, M.P., Chang, Q., Li, Y., Xiao, G. and Arinez, J. (2014). Energy efficiency management of an integrated serial production line and HVAC system. IEEE Transactions on Automation Science and Engineering, 11(3), 789-797. Bonneschky, A. (2002). Integration energiewirtschaftlicher Aspekte in Systeme der Produktionsplanung und steuerung. Dissertation, University Cottbus. Dong, B., and Andrews, B. (2009). Sensor-based occupancy behavioral pattern recognition for energy and comfort management in intelligent buildings. Proceedings of building simulation, 1444-1451. Eisele, C., Schrems, S. and Abele, E. (2011). Energy-efficient machine tools through simulation in the design process. In Hesselbach J., Herrmann C. (ed.), Glocalized Solutions for Sustainability in Manufacturing, 258-262. Springer, Berlin, Heidelberg. Fang, K., Uhan, N., Zhao, F. and Sutherland, J.W. (2011). A new shop scheduling approach in support of sustainable manufacturing. In Hesselbach J., Herrmann C. (ed.), Glocalized Solutions for Sustainability in Manufacturing, 305-310. Springer, Berlin, Heidelberg. Giffler, B. and Thompson, G.L. (1960). Algorithms for solving production-scheduling problems. Operations Research, 8 (4), 487-503. Goldberg, D. E. and Deb, K. (1991). A comparative analysis of selection schemes used in genetic algorithms. In

The literature review identifies the existing gap in production environment regarding an integrated energy-efficiency approach. A practicable solution that combines both mayor energy consumers, production activities and building automation, is required. For generating a foundation for the development of an integrated energy-control-loop approach, two state-of-the-art approaches are presented from the research projects eMES and BIG. Based on this, an integrated energy control loop is presented that merges both concepts. It considers the three thermal streams (production activities, atmosphere, and building facilities), contains a multi-objective decision mechanism including a multidimensional cost function, and results into four controlling strategies. Ultimately, an integrated approach for an energy-efficient production has been developed using a control loop. ACKNOWLEDGEMENTS This paper is a result of the following research projects: eMES − Energy-Oriented Production Control as well as Machine Weather Forecast Disturbance values

MES

Air Temperature/Sunshine

Energy Predictor

Building Automation

Control Variables Schedules of Orders

Control Variables Energy Controller

Reference Variables Delivery dates, Room temperature Production Process

On/Off plan of HAVC

Deviations

As-IsComparison

Control and Monitoring for Integration into a Manufacturing Execution System is funded within the BMBF funding measure KMU-innovative: Information and Communication Technologies under the grant number 01IS14025A-D. BIG − is funded within the BMBF funding measure KMUinnovative: resource and energy efficiency under the grant number 01LY1510B. REFERENCES

5. CONCLUSIONS

Orders

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Production Environment

Fig. 5. Holistic energy efficiency by an energetic control loop. 143

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Rawlins, G.J.E. (ed.), Foundations of Genetic Algorithms, 69-93. Elsevier Science, Burlington. Große Böckmann, M. (2014). Senkung der Produktionskosten durch Gestaltung eines Energieregelkreis-Konzeptes. Dissertation, University Aachen. Haag, H., Siegert, J., Bauernhansl, T. and Westkämper, E. (2012). An approach for the planning and optimization of energy consumption in factories considering the peripheral systems. Leveraging Technology for a Sustainable World, 335-339. Springer, Berlin, Heidelberg. Henze, G. P., Felsmann, C., and Knabe, G. (2004). Evaluation of optimal control for active and passive building thermal storage. International Journal of Thermal Sciences, 43(2), 173-183. Heutmann, T. and Schmitt, R. (2017). Energieorientierte Produktionsplanung und -steuerung. In Zeitschrift für wirtschaftlichen Fabrikbetrieb ZWF, 112 (9), 563-566. Junge, M. (2007). Simulationsgestützte Entwicklung und Optimierung einer energieeffizienten Produktionssteuerung. Dissertation, University Kassel. Kahn, M., Carreras, F., Hettfleisch, C., Gstach, E. and Knorr, D. (2014). Energieeffizienzsteigerung in der automatisierten Gebäudeklimatisierung durch wetterprognoseunterstützte Regelung (ProKlim). Kara, S. and Li, W. (2011). Unit process energy consumption models for material removal processes. CIRP Annals, 60 (1), 37-40. Khalaf, S. (2012). Entwicklung eines Vorgehensmodells zur Erstellung energieeffizienter Fertigungsstrategien für verkettete Fertigungssysteme. Dissertation, University Bochum. Khan, K. H., Ryan, C. and Abebe, E. (2017). Optimizing HVAC energy usage in industrial processes by scheduling based on weather data. IEEE Access, 5, 11228-11235. Krings, R. (2012). Bewertung und kontinuierliche Verbesserung der Ressourceneffizienz von automatisierten Fertigungssystemen. Dissertation, University Bochum. Ma, Y., Borrelli, F., Hencey, B., Packard, A., and Bortoff, S. (2009). Model predictive control of thermal energy storage in building cooling systems. In Proc. IEEE CDC, 392-397. Martin, L., Hesselbach, J., Thiede, S., Herrmann, C., Lüdemann, B., and Detzer, R. (2008). Energieeffizienz durch optimierte Abstimmung zwischen Produktion und technischer Gebäudeausrüstung. In Rabe, M. (ed.), Advances in Simulation for Production and Logistics Applications, 177-185, Fraunhofer IRB Verlag, Stuttgart. Oldewurtel, F., Parisio, A., Jones, C. N., Gyalistras, D., Gwerder, M., Stauch, V., Lehmann, B. and Morari, M. (2012). Use of model predictive control and weather forecasts for energy efficient building climate control. Energy and Buildings, 45 (2012), 15-27. Pechmann, A. and Schöler, I. (2011). Optimizing energy costs by intelligent production scheduling. In Hesselbach J., Herrmann C. (ed.), Glocalized Solutions for Sustainability in Manufacturing, 293-298. Springer, Berlin, Heidelberg. Rager, M. (2008). Energieorientierte Produktionsplanung. Gabler Verlag, Wiesbaden.

Razali, N.M. and Geraghty, J. (2011). Genetic algorithm performance with different selection strategies in solving TSP. In Ao, S.I., Gelman, L., Hunkings, D. W. L., Hunter, A. and Korsunsky, A. M. (ed.), Proc. WCE 2011, 2 (2011), 1134-1139. Schultz, C., Sellmaier, P. and Reinhart, G. (2015). An approach for energy-oriented production control using energy flexibility. Procedia CIRP, 29 (2015), 197-202. Shukla, A., Pandey, H.M. and Mehrotra, D. (2015). Comparative review of selection techniques in genetic algorithm. In Singh, G. (ed.), 2015 ABLAZE, 515-519. IEEE, Piscataway, NJ. Široký, J., Oldewurtel, F., Cigler, J., and Prívara, S. (2011). Experimental analysis of model predictive control for an energy efficient building heating system. Applied Energy, 88(9), 3079-3087. Sobottka, T., Kamhuberab, F., Rösslerc, M., and Sihnab, W. (2018). Hybrid simulation-based optimization of discrete parts manufacturing to increase energy efficiency and productivity. In Procedia manufacturing 21 (2018), 413420. Sun, Z., Li, L. and Dababneh, F. (2016). Plant-level electricity demand response for combined manufacturing system and heating, venting, and air-conditioning (HVAC) system. Journal of cleaner production, 135, 1650-1657. Thamilselvan, R. and Balasubramanie, P. (2012). Integrating genetic algorithm, tabu search approach for job shop scheduling. In IJCA, 48(5), 42-54. Thiede, S. (2011). Energy efficiency in manufacturing systems. Dissertation, University Braunschweig. UBA − German Environment Agency (2018). Branchenabhängiger Energieverbrauch des verarbeitenden Gewerbes. German Environment Agency, DessauRoßlau. Verl, A., Abele, E., Heisel, U., Dietmair, A., Eberspächer, P., Rahäuser, R., Schrems, S. and Braun, S. (2011a). Modular modeling of energy consumption for monitoring and control. In Hesselbach J., Herrmann C. (ed.), Glocalized Solutions for Sustainability in Manufacturing, 341-346, Springer, Berlin, Heidelberg. Verl, A., Westkämper, E., Abele, E., Dietmair, A., Jan, S., Friedrich, J., Haag, H. and Schrems, S. (2011b). Architecture for multilevel monitoring and control of energy consumption. In Hesselbach J., Herrmann C. (ed.), Glocalized Solutions for Sustainability in Manufacturing, 347-352, Springer, Berlin, Heidelberg. Weinert, N., Chiotellis, S. and Seliger, G. (2011). Methodology for planning and operating energy-efficient production systems. In CIRP Annals, 60 (1), 41–44. Wenzel, K., Riegel, J., Schlegel, A. and Putz, M. (2011). Semantic Web based dynamic energy analysis and forecasts in manufacturing engineering. In Hesselbach J., Herrmann C. (ed.), Glocalized Solutions for Sustainability in Manufacturing, 507-512, Springer, Berlin, Heidelberg. Yang X.S. (2010). Engineering optimization: an introduction with metaheuristic applications. John Wiley & Sons, Wiley, Hoboken, NJ.

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