Format And Type Fonts CCHHEEMMIICCAALL EENNGGIINNEEEERRIINNGG TTRRAANNSSAACCTTIIOONNSS VOL. 45, 2015 A publication of The Italian Association of Chemical Engineering www.aidic.it/cet Guest Editors: Petar Sabev Varbanov, Jiří Jaromír Klemeš, Sharifah Rafidah Wan Alwi, Jun Yow Yong, Xia Liu Copyright © 2015, AIDIC Servizi S.r.l., ISBN 978-88-95608-36-5; ISSN 2283-9216 DOI: 10.3303/CET1545183 Please cite this article as: Wu L., Liu Y. , Kang L., 2015, Multi-objective optimization on driving options for rotating equipment in process industries to make trade-offs between economy and environmental impacts, Chemical Engineering Transactions, 45, 1093-1098 DOI:10.3303/CET1545183 1093 Multi-objective Optimization on Driving Options for Rotating Equipment in Process Industries to Make Trade-offs between Economy and Environmental Impacts Le Wu, Yongzhong Liu*, Lixia Kang Department of Chemical Engineering, Xi’an Jiaotong University, Xi’an, 710049, China yzliu@mail.xitu.edu.cn In process industries, steam turbines and electric motors are usually selected to be the choice of drivers for pumps and compressors. The economy of the drivers is usually one of the critical factors when the decisions of drives for pumps and compressors are made. In this work, we proposed a multi-objective optimization model to simultaneously minimize the total annual costs and environmental impacts. In the proposed model, the capital and operational costs for the drivers were included in the economic objective and the environmental impacts was measured by the indexes of Eco-indicator 99. The driving system in a diesel hydrotreating unit was taken as a case study to exemplify the proposed method. Under the circumstances of fixed and variable prices of electricity and steam, the influences on Pareto fronts and trade-off points were obtained and discussed. Results show that the steam turbines are favorable in the driving system when the environmental impacts are mainly considered. The drives option schemes are sensitive to the change of electricity and steam prices, and the environmental impacts of the drives option should not be ignored. 1. Introduction In process industries, pumps and compressors are commonly employed to deliver fluids or increase pressure. Steam turbines and electric motors are usually selected to be the choice of drivers for pumps and compressors (Hugot, 2014). The drives option of the driving system in a plant is of importance to determine utility consumption, capital cost and operational cost as well. Conventionally, the economy of the drivers is one of the critical factors when the decisions of drives for pumps and compressors are made. For example, the objective of the drives option is to minimize total annual cost (TAC) of driving systems. Oh and Yeo (2008) obtained the minimum TAC of the driving system through appropriate application of electric motors and steam turbines by solving a mixed integer nonlinear programming (MINLP) problem. Li et al. (2014) proposed a MINLP model to optimize a steam system in a chemical plant that contains multiple direct drive steam turbines by considering electric power as the alternative energy source for lower-level mechanical power demands. However, these previous studies mainly focused on the economy of the drives option instead of considering impacts of both economy and environment. Furthermore, the influence of different electricity and steam prices on the drives option is often ignored. To deal with the problems mentioned above, we proposed a multi-objective optimization model aiming at simultaneously minimizing the economic objective and environmental objective of the drives option for rotating equipment in process Industries. In the model, life cycle assessment (LCA) method is used to quantify the environmental impacts. Moreover, the influences of different electricity and steam prices on the drives option are also analysed and discussed. 1094 2. Multi-objective optimization model 2.1 Economic objective For a driving system with M electric motors and N steam turbines, the total annual cost for the drives option is chosen as the economic objective, which contains the capital cost of the electric motors and steam turbines and the operating cost of utilities consumed by these drivers. It can be expressed as   1 1 M N m t elec elec hs hs ls ls m i t j i j TAC f C f C u C u C u C                 (1) where mf and tf denote the spare factors of the electric motor and steam turbine; m iC and t jC represent the annualized investment cost of motor and steam turbine, [CNYy -1 ]. CNY is the currency of China. elecu , hs u and ls u are the annual consumption of electricity, [kWhy -1 ], the annual consumption amount of high pressure steam and low pressure steam, [ty -1 ]. elecC , hs C and ls C represent the price of electricity, [CNYkWh -1 ], the prices of high pressure steam and low pressure steam, [CNYt -1 ]. Since the low pressure steam discharged from the steam turbine is usually delivered to the low pressure steam header, the total operating cost should take out the cost of low pressure steam. The annualized investment cost of the electric motor and the steam turbine can be calculated by (Chen and Lin, 2012) 9766.1 166.457 m m i iC W  (2) 497723.4 110.117 t t j jC W  (3) where m iW denotes the driving power of the i th electric motor, kW, and t jW denotes the driving power of the j th steam turbine in the driving system, [kW]. The electricity consumption in the driving system can be calculated by 1 mM elec i ii W u t     (4) where t is the annual operating time, and i is the efficiency of the i th electric motor. The consumption of the high pressure steam in the driving system can be expressed as   1 tN jhs hs ls jj W u t H H     (5) where hsH and ls H are the enthalpies of high pressure steam and low pressure steam, [kJt -1 ]; j denotes the efficiency of the j th steam turbine. Assume that the leakage and loss of steam are ignored. Then the amount of low pressure steam equals to the amount of the high pressure steam consumed by the steam turbines. That is hs ls u u (6) 2.2 Environmental objective Life cycle assessment (LCA) is a method to quantitatively assess the environmental impacts of goods and processes from “cradle to grave.”(Hellweg and Canals, 2014) The impacts of the consumed electricity and steam on the eco-system can be measured by the indexes in Eco-indicator 99 (Goedkoop and Spriensma, 2000), which is based on LCA. According to the instructions of Eco-indicator 99, the environmental impacts of the consumed electricity and steam can be expressed as elec elec hs hs ls ls Env u DAM u DAM u DAM   (7) where DAM denotes the damage factor of utilities, which can be obtained from the database of Eco- indicator 99 (Sander Hegger, 2010). The units are [PtkWh -1 ] for electricity and [Ptt -1 ] for steam. 1095 2.3 Trade-off between the economic objective and the environmental objective For this multi-objective optimization problem, each optimal solution on the Pareto front can be considered as the trade-off solution of the economic and environmental objectives. Thus, a linear membership function  can be used to describe weights of the objective functions.(Agrawal et al., 2008), which can be expressed as    max max mins so o o o oO O O O    (8) where max oO and min oO denote the maximum and minimum values of the o th objective function among all solutions. And s oO denotes the solution of the o th objective function. The membership function of each Pareto-optimal solution can be expressed as s s o o o     (9) where o is the weight value of the o th objective function that is optional for decision makers. Then the trade-off solution is the one corresponding to the maximum of s . 3. Case study In this section, the driving system in a diesel hydrotreating (HDT) unit with an annual processing capacity of 3.2 × 10 6 t is taken as an example to simultaneously minimize its TAC and the environmental objective. Figure 1 shows the flowchart of the HDT unit. Table 1 lists the rated powers of pumps and compressors. The damage factors of utilities are given in Table 2. The enthalpy of the high pressure steam and the one of the low pressure steam are 3.189 kJt -1 and 2.777 kJt -1 . The annual running time of the unit is 8,400 h. To simplify the calculations, we assume the efficiencies of electric motors and steam turbines to be 75 % and 65 %. 9 Pump Absorber Stripper 4 Pump Fractio- nator 8 Compressor 10 Compressor Feed Make-up H2 Separators Acid water Water Steam MDEA Rich MDEA 7 Pump Reactor 1 Pump 5 Pump Diesel 6 Pump 3 Pump Naphtha 2 Pump Figure 1: The flowchart of a diesel HDT unit Table 1: The rated powers of pumps and compressors No. Pump or compressor Rated power/ kW 1 Sour water pump 9.9 2 Reflux pump of stripper 29.2 3 Reflux pump of fractionator 47.8 4 Water pump 76.5 5 Refined diesel pump 215 6 Circulating pump of fractionator 414 7 Poor MEDA pump 497 8 Cycle hydrogen compressor 1,960 9 Feed pump 2,277 10 Make-up hydrogen compressor 2,800 1096 Table 2: Damage factors of electricity and steam elecDAM / PtkWh -1 hs DAM / Ptt -1 ls DAM / Ptt -1 Damage factor 0.7873 15.009 13.07 3.1 Drives option at the fixed prices of electricity and steam Assume that the electricity price is 0.52 CNYkWh -1 , and the price of high pressure and low pressure steam are 120 CNYt -1 and 90 CNYt -1 . The proposed multi-objective optimization model was solved by the  -constraint method implemented by the software package GAMS, where the weight values of the two objectives in Eq(9) are 0.5. The maximum value of Eq(9) is the trade-off point. The obtained Pareto front of the two objectives is shown in Figure 2, and the schemes of drives option are listed in Table 3. 2 4 6 8 10 12 14 50 51 52 53 54 55 56 57 Pareto Front Trade-off T A C / 1 0 6 C N Y y -1 Env/10 6 Pty -1 Figure 2: Pareto front of TAC and the environmental objective Figure 2 indicates that the economic and environmental objectives present an opposite trend. TAC reaches its minimum of 50.26 × 10 6 CNY while the annual environmental impacts are the maximum of 13.74 × 10 6 Pt. On the contrary, the annual environmental impacts gets the minimum of 2.805 × 10 6 Pt while TAC reaches its maximum of 56.17 × 10 6 CNY. The red dot in Figure 2 denotes the trade-off solution, which corresponds to the TAC of 5.238 × 10 7 CNY and the annual environmental impact of 4.191 × 10 6 Pt. The schemes of drives option of the three cases are listed in Table 3. Table 3: Schemes of drives option at fixed electricity and steam prices No. 1 2 3 4 5 6 7 8 9 10 Min TAC M M M M M M M T T T Min Env T T T T T T T T T T Trade-off M M M M T T T T T T Note: The number coincides with the number in Table 1; M and T denote the electric motor and the steam turbine. When the TAC of the driving system is selected as the dominant objective, Table 3 indicates that motor- driven pumps or compressors are highly recommended when the rated powers are less than 500 kW. In this case, the high investment cost of steam turbines can be compensated by the less operating cost of steam. On the other hand, when the environmental objective is mainly considered, the steam turbine- driven pumps or compressors are recommended because the environmental impacts of steam are less than that of electricity. However, the motor-driven rotating equipment could be a better choice when their rated powers are less than 80 kW, which corresponds to the trade-off point. 3.2 Drives option under different electricity price Since the electricity price varies with the fuel price, it is necessary to study the influence of utility prices change on the drives option. Figure 3 shows the Pareto fronts of economy and environmental objectives for drives option varying with the electricity prices varying from 0.49 CNYkWh -1 , 0.55 CNYkWh -1 to 0.65 CNYkWh -1 . As shown in Figure 3, the minimum TAC increases and the maximum environmental impacts decrease with the 1097 increase of electricity price. In other words, the Pareto optimal solutions and trade-off solution are dramatically dependent on the electricity price. The optimal schemes of drives option are given in Table 4. These results indicate that the steam turbines will reduce both TAC and environmental impacts of the driving system as the electricity price increases. 0 11 22 33 44 55 48 50 52 54 56 58 T A C / 1 0 6 C N Y y -1 Env/10 6 Pty -1 0.49 0.55 0.65 Trade-off Electricity Price (CNY/kWh) Figure 3: Pareto fronts under different electricity price Table 4: Schemes of drives option under different electricity price Electricity Price/ CNYkWh -1 No. 1 2 3 4 5 6 7 8 9 10 0.49 Min TAC M M M M M M M M M T Min Env T T T T T T T T T T Trade-off M M M M M M M T T T 0.55 Min TAC M M M M M M M T T T Min Env T T T T T T T T T T Trade-off M M M M T T T T T T 0.65 Min TAC M M M M M M T T T T Min Env T T T T T T T T T T Trade-off M M M M T T T T T T 3.3 Drives option under different steam price The change of steam prices will also affect the drives option in the driving system. 0 10 20 30 40 50 60 70 80 45 50 55 60 65 118 122 124 Trade-off T A C / 1 0 6 C N Y y -1 Env/10 6 Pty -1 High PressureSteam Price (CNY/ton) Figure 4 Pareto fronts under different steam prices Likewise, as shown in Figure 4, we can also obtain the Pareto fronts of TAC and environmental objective for drives option with high pressure steam prices changing from 118 CNYt -1 , 122 CNYt -1 to 124 CNYt -1 , whereas the low pressure steam price holds constant of 90 CNYt -1 . 1098 As shown, both the minimum TAC and maximum environmental impacts increase as the price of high pressure steam increases from 118 CNYt -1 to 124 CNYt -1 . This trend is opposite to that presented in Figure 3. When the price of high pressure steam increases, the steam turbines would increase the TAC and reduce the environmental impacts. Therefore, the variation of high pressure steam price has intensive influences on the Pareto front and trade-off solution as well. The schemes of drives option after optimization are given in Table 5. Table 5: Schemes of drives option under different steam prices HP Steam Price / CNYt -1 No. 1 2 3 4 5 6 7 8 9 10 118 Min TAC M M M M M M M T T T Min Env T T T T T T T T T T Trade-off M M M M T T T T T T 122 Min TAC M M M M M M M M M T Min Env T T T T T T T T T T Trade-off M M M M M M M T T T 124 Min TAC M M M M M M M M M M Min Env T T T T T T T T T T Trade-off M M M M M M M T T T 4. Conclusions In this work, we proposed a multi-objective optimization model to simultaneously minimize the economic objective and environmental objective of the drives option for rotating equipment in process Industries, in which the environmental impacts of the driving system are measured by the indexes of Eco-indicator 99. The influences of fixed and variable prices of the electricity and steam on the schemes of drives option in the driving system are analyzed and discussed. The results for the driving system in a diesel hydrotreating unit show that appropriate drives option could reduce both the TAC and environmental impacts. When the environmental impacts are mainly considered, the steam turbines are favorable in the driving system. The variations of electricity and steam prices should be taken into consideration due to the fact that the drives option scheme is sensitive to these changes. Acknowledgements The authors gratefully acknowledge funding by the project (No.21376188 and No.2015GY095) sponsored by the Natural Science Foundation of China and the Industrial Science & Technology Planning Project of Shaanxi Province. References Agrawal S., Panigrahi K.B., Tiwari M.K., 2008, Multiobjective Particle Swarm Algorithm With Fuzzy Clustering for Electrical Power Dispatch, Evolutionary Computation, IEEE Transactions on Evolutionary Computation, 12, 529-541. Chen C.-L., Lin C.-Y., 2012, Design of Entire Energy System for Chemical Plants. Industrial & Engineering Chemistry Research, 51, 9980-9996. Goedkoop M., Spriensma R., 2000, The Eco-indicator 99 Methdology Report., 2nd Eds., PRé-Consultants, Amersfoort, the Netherlands. Hellweg S., Canals L.M., 2014, Emerging Approaches, Challenges and Opportunities in Life Cycle Assessment, Science, 344, 1109-1113. Hugot E., 2014, Handbook of Cane Sugar Engineering 3rd Eds., Elsevier, New York, USA. Li Z., Du W., Zhao L., Qian F., 2014, Modeling and Optimization of a Steam System in a Chemical Plant Containing Multiple Direct Drive Steam Turbines, Industrial and Engineering Chemistry Research, 53, 11021-11032. Oh S., Yeo Y., 2008, Modeling and Simulation of Motor/Turbine Processes in Utility Plant, Korean Journal of Chemical Engineering, 25, 409-418. Hegger S., Hischier R., 2010, Assignment of Characterisation Factors to the Swiss LCI Database Ecoinvent, 2nd Eds., PRé-Consultants, Amersfoort, the Netherlands.