Assessment and optimization of environmental systems using data analysis and simulation.

For most environmental systems, specifically wastewater treatment plants and aquifers, a significant number of performance data variables are attained on a time series basis. Due to the interconnectedness of the variables, it is often difficult to assess over-arching trends and quantify temporal operational performance. The objective of this research study was to provide an effective means for comprehensive temporal evaluation of environmental systems. The proposed methodology used several multivariate data analyses and statistical techniques to present an assessment framework for the water quality monitoring programs as well as optimization of treatment plants and aquifer systems. The developed procedure considered the combination of statistical and data analysis algorithms including correlation techniques, factor analysis and principal component analysis, and multivariate stepwise regression analysis. Those methodologies were used to develop a series of independent indexes to quantify the composition of wastewater and groundwater. Also, by developing a stepwise data analysis approach, a baseline was introduced to discover the key operational parameters which significantly affect the performance of environmental systems. Moreover, a comprehensive approach was introduced to develop numerical models for forecasting key operational and quality parameters which can be used for future simulation and scenario analysis practices. The developed methodology and frameworks were successfully applied to four case studies which include three wastewater treatment plants and an aquifer system. In the first case study, the aforementioned approach was applied to the Floyds Fork water quality treatment center in Louisville, KY. The objective of this case study was to establish simple and reliable predictive models to correlate target variables with specific measured parameters. The study presented a multivariate statistical and data analyses of the wastewater physicochemical parameters to provide a baseline for temporal assessment of the treatment plant. Fifteen quality and quantity parameters were analyzed using data recorded from 2010 to 2016. To determine the overall quality condition of raw and treated wastewater, a Wastewater Quality Index (WWQI) was developed. To identify treatment process performance, the interdependencies between the variables were determined by using Principal Component Analysis (PCA). The five extracted components adequately represented the organic, nutrient, oxygen demanding, and ion activity loadings of influent and effluent streams. The study also utilized the model to predict quality parameters such as Biological Oxygen Demand (BOD), Total Phosphorus (TP), and WWQI. High accuracies ranging from 71% to 97% were achieved for fitting the models with the training dataset and relative prediction percentage errors less than 9% were achieved for the testing dataset. The presented techniques and procedures in this case study provide an assessment framework for the wastewater treatment monitoring programs. The second case study focused on assessing methane production of a novel combined system for treatment of high strength organic wastewater. The studied pilot plant comprised Rotating Biological Contactor (RBC) process under anaerobic condition, in conjunction with Moving Bed Biofilm Reactor (MBBR) as the combining aerobic process. Various operational parameters were tested to maximize the Chemical Oxygen Demand (COD) removal performance and methane gas production from treating high strength synthetic wastewater. The identified optimal parameters included hydraulic retention time, organic loading rate, and disk rotational speed; equal to 5 days, 7 rpm, and 2 kg COD/m3/d, respectively. Under these conditions, the combined system achieved high removal efficiency (98% from influent COD of 10,000 mg/L) with additional benefit of methane production (116.60 L/d from a 46-liter AnRBC reactor). The obtained results from conducting this case study confirmed the effectiveness of integrated hybrid system in achieving both high removal efficiency and methane production. Thus, this system was recommended for treating high strength organic wastewater. The third case study focused on assessing the feasibility of using a contact stabilization process for secondary treatment of refinery wastewater through a step by step analysis. the studied pilot plant comprised contact-stabilization activated sludge process in conjunction with clarification reactor. Various operational parameters were tested to minimize excessive sludge production and maximize system removal performance from treating petroleum refinery wastewater. The mixed liquor dissolved oxygen (DO) and the rate of activated return sludge (RS) were selected as key operational parameters. The results indicated that the system had an optimum performance under applied aeration of 3.7 mg oxygen per liter of mixed liquor and 46% return sludge. This operational combination resulted in COD removal efficiency of 78% with daily biomass production of 1.42 kg/day. Considering the results from this case study, the contact stabilization activated sludge process was suggested as an effective alternative for secondary treatment of wastewater from petroleum refineries. The last case study combined probabilistic and deterministic approaches for assessing aquifer’s water quality. The probabilistic approach used multivariate statistical analysis to classify the groundwater’s physiochemical characteristics. Building upon the obtained results, the deterministic approach used hydrochemistry analyses for a more comprehensive assessment of groundwater suitability for different applications. For this purpose, a large geologic basin, under arid weather conditions, was evaluated. The ultimate objective was to identify: 1) groundwater classification scheme, 2) processes governing the groundwater chemistry, 3) hydrochemical characteristics of groundwater, and 4) suitability of the groundwater for drinking and agricultural purposes. Considering the results from multivariate statistical analysis, chloride salts dissolution was identified within the aquifer. Further application of the deterministic approach revealed degradation of groundwater quality throughout the basin, possibly due to the saltwater intrusion. By developing the water quality index and a multi-hazard risk assessment methodology, the suitability of groundwater for human consumption and irrigation purposes were assessed. The combined consideration of deterministic and probabilistic approaches provided an effective means for comprehensive evaluation of groundwater quality across different aquifers or within one. The presented procedures and methodologies in this research study provide environmental analysts and governmental decision makers with a comprehensive tool to evaluate current and future quality conditions within any given wastewater treatment plants and/or aquifer systems

Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model

[EN] Lack of homogeneity in the product (LHP) appears in some production processes that confer heterogeneity in the characteristics of the products obtained. Supply chains with this issue have to classify the product in different homogeneous subsets, whose quantity is uncertain during the production planning process. This paper proposes a generic framework for reviewing in a unified way the literature about production planning models dealing with LHP uncertainty. This analysis allows the identification of similarities among sectors to transfer solutions between them and gaps existing in the literature for further research. The results of the review show: (1) sectors affected by LHP inherent uncertainty, (2) the inherent LHP uncertainty types modelled, and (3) the approaches for modelling LHP uncertainty most widely employed. Finally, we suggest a conceptual model reflecting the aspects to be considered when modelling the production planning in sectors with LHP in an uncertain environment.This research was initiated within the framework of the project funded by the Ministerio de Economía y Competitividad [Ref. DPI2011-23597] entitled ‘Methods and models for operations planning and order management in supply chains characterised by uncertainty in production due to the lack of product uniformity’ (PLANGES-FHP) already finished. After, the project leading to this application has received funding from the European Union’s research and innovation programme under the H2020 Marie Skłodowska-Curie Actions with the grant agreement No 691249, Project entitled ’Enhancing and implementing Knowledge based ICT solutions within high Riskand Uncertain Conditions for Agriculture Production Systems’ (RUC-APS).Mundi, I.; Alemany Díaz, MDM.; Poler, R.; Fuertes-Miquel, VS. (2019). Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model. International Journal of Production Research. 57(15-16):5239-5283. https://doi.org/10.1080/00207543.2019.1566665S523952835715-16Ahumada, O., Rene Villalobos, J., & Nicholas Mason, A. (2012). Tactical planning of the production and distribution of fresh agricultural products under uncertainty. Agricultural Systems, 112, 17-26. doi:10.1016/j.agsy.2012.06.002Ahumada, O., & Villalobos, J. R. (2009). Application of planning models in the agri-food supply chain: A review. European Journal of Operational Research, 196(1), 1-20. doi:10.1016/j.ejor.2008.02.014Alarcón, F., Alemany, M. M. E., Lario, F. C., & Oltra, R. F. (2011). La falta de homogeneidad del producto (FHP) en las empresas cerámicas y su impacto en la reasignación del inventario. Boletín de la Sociedad Española de Cerámica y Vidrio, 50(1), 49-58. doi:10.3989/cyv.072011Albornoz, V. M., M. González-Araya, M. C. Gripe, and S. V. Rodrıguez. 2014. “A Mixed Integer Linear Program for Operational Planning in a Meat Packing Plant.” Accessed January 15, 2015. http://www.researchgate.net/profile/Victor_Albornoz/publication/268687089_A_Mixed_Integer_Linear_Program_for_Operational_Planning_in_a_Meat_Packing_Plant/links/547382bf0cf29afed60f55c7.pdf.José Alem, D., & Morabito, R. (2012). Production planning in furniture settings via robust optimization. Computers & Operations Research, 39(2), 139-150. doi:10.1016/j.cor.2011.02.022Alemany, M. M. E., Lario, F.-C., Ortiz, A., & Gómez, F. (2013). Available-To-Promise modeling for multi-plant manufacturing characterized by lack of homogeneity in the product: An illustration of a ceramic case. Applied Mathematical Modelling, 37(5), 3380-3398. doi:10.1016/j.apm.2012.07.022Alemany, M., Ortiz, A., & Fuertes-Miquel, V. S. (2018). A decision support tool for the order promising process with product homogeneity requirements in hybrid Make-To-Stock and Make-To-Order environments. Application to a ceramic tile company. Computers & Industrial Engineering, 122, 219-234. doi:10.1016/j.cie.2018.05.040Alfalla-Luque, R., Medina-Lopez, C., & Dey, P. K. (2012). Supply chain integration framework using literature review. Production Planning & Control, 24(8-9), 800-817. doi:10.1080/09537287.2012.666870Al-Othman, W. B. E., Lababidi, H. M. S., Alatiqi, I. M., & Al-Shayji, K. (2008). Supply chain optimization of petroleum organization under uncertainty in market demands and prices. European Journal of Operational Research, 189(3), 822-840. doi:10.1016/j.ejor.2006.06.081Al-Shammari, A., & Ba-Shammakh, M. S. (2011). Uncertainty Analysis for Refinery Production Planning. Industrial & Engineering Chemistry Research, 50(11), 7065-7072. doi:10.1021/ie200313rAmaro, A. C. S., & Barbosa-Póvoa, A. P. F. D. (2009). The effect of uncertainty on the optimal closed-loop supply chain planning under different partnerships structure. Computers & Chemical Engineering, 33(12), 2144-2158. doi:10.1016/j.compchemeng.2009.06.003ARAS, N., BOYACI, T., & VERTER, V. (2004). The effect of categorizing returned products in remanufacturing. IIE Transactions, 36(4), 319-331. doi:10.1080/07408170490279561Aydin, R., Kwong, C. K., Geda, M. W., & Okudan Kremer, G. E. (2017). Determining the optimal quantity and quality levels of used product returns for remanufacturing under multi-period and uncertain quality of returns. The International Journal of Advanced Manufacturing Technology, 94(9-12), 4401-4414. doi:10.1007/s00170-017-1141-0Bakhrankova, K., Midthun, K. T., & Uggen, K. T. (2014). Stochastic optimization of operational production planning for fisheries. Fisheries Research, 157, 147-153. doi:10.1016/j.fishres.2014.03.018Banasik, A., Kanellopoulos, A., Claassen, G. D. H., Bloemhof-Ruwaard, J. M., & van der Vorst, J. G. A. J. (2017). Closing loops in agricultural supply chains using multi-objective optimization: A case study of an industrial mushroom supply chain. International Journal of Production Economics, 183, 409-420. doi:10.1016/j.ijpe.2016.08.012Beaudoin, D., LeBel, L., & Frayret, J.-M. (2007). Tactical supply chain planning in the forest products industry through optimization and scenario-based analysis. Canadian Journal of Forest Research, 37(1), 128-140. doi:10.1139/x06-223Begen, M. A., & Puterman, M. L. (2003). Development Of A Catch Allocation Tool Design For Production Planning At Js Mcmillan Fisheries. INFOR: Information Systems and Operational Research, 41(3), 235-244. doi:10.1080/03155986.2003.11732678Benedito, E., & Corominas, A. (2010). Optimal manufacturing and remanufacturing capacities of systems with reverse logistics and deterministic uniform demand. Journal of Industrial Engineering and Management, 3(1). doi:10.3926/jiem.2010.v3n1.p33-53Bertrand, J. W. ., & Rutten, W. G. M. . (1999). Evaluation of three production planning procedures for the use of recipe flexibility. European Journal of Operational Research, 115(1), 179-194. doi:10.1016/s0377-2217(98)00166-0Björheden, R., & Helstad, K. (2005). Raw Material Procurement in Sawmills’ Business Level Strategy-A Contingency Perspective. International Journal of Forest Engineering, 16(2), 47-56. doi:10.1080/14942119.2005.10702513Bohle, C., Maturana, S., & Vera, J. (2010). A robust optimization approach to wine grape harvesting scheduling. European Journal of Operational Research, 200(1), 245-252. doi:10.1016/j.ejor.2008.12.003Cai, X., Lai, M., Li, X., Li, Y., & Wu, X. (2014). Optimal acquisition and production policy in a hybrid manufacturing/remanufacturing system with core acquisition at different quality levels. European Journal of Operational Research, 233(2), 374-382. doi:10.1016/j.ejor.2013.07.017Carneiro, M. C., Ribas, G. P., & Hamacher, S. (2010). Risk Management in the Oil Supply Chain: A CVaR Approach. Industrial & Engineering Chemistry Research, 49(7), 3286-3294. doi:10.1021/ie901265nChakraborty, M., & Chandra, M. K. (2005). Multicriteria decision making for optimal blending for beneficiation of coal: a fuzzy programming approach. Omega, 33(5), 413-418. doi:10.1016/j.omega.2004.07.005LUO, C., & RONG, G. (2009). A Strategy for the Integration of Production Planning and Scheduling in Refineries under Uncertainty. Chinese Journal of Chemical Engineering, 17(1), 113-127. doi:10.1016/s1004-9541(09)60042-2Davoli, G., Gallo, S., Collins, M., & Melloni, R. (2011). A stochastic simulation approach for production scheduling and investment planning in the tile industry. International Journal of Engineering, Science and Technology, 2(9). doi:10.4314/ijest.v2i9.64006Denizel, M., Ferguson, M., & Souza, G. (2010). Multiperiod Remanufacturing Planning With Uncertain Quality of Inputs. IEEE Transactions on Engineering Management, 57(3), 394-404. doi:10.1109/tem.2009.2024506Dong, M., Lu, S., & Han, S. (2011). Production Planning for Hybrid Remanufacturing and Manufacturing System with Component Recovery. Advances in Electrical Engineering and Electrical Machines, 511-518. doi:10.1007/978-3-642-25905-0_66Dubois, D., Fargier, H., & Fortemps, P. (2003). Fuzzy scheduling: Modelling flexible constraints vs. coping with incomplete knowledge. European Journal of Operational Research, 147(2), 231-252. doi:10.1016/s0377-2217(02)00558-1DUENYAS, I., & TSAI, C.-Y. (2000). Control of a manufacturing system with random product yield and downward substitutability. IIE Transactions, 32(9), 785-795. doi:10.1080/07408170008967438Esteso, A., Alemany, M. M. E., & Ortiz, A. (2018). Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. International Journal of Production Research, 56(13), 4418-4446. doi:10.1080/00207543.2018.1447706French, M. L., & LaForge, R. L. (2005). Closed-loop supply chains in process industries: An empirical study of producer re-use issues. Journal of Operations Management, 24(3), 271-286. doi:10.1016/j.jom.2004.07.012Gallo, M., R. Grisi, G. Guizzi, and E. Romano. 2009. “A Comparison of Production Policies in Remanufacturing Systems,” Proceedings of the 8th WSEAS International Conference on System Science and Simulation in Engineering, ICOSSSE ‘09, pp. 334.Goodfellow, R., & Dimitrakopoulos, R. (2017). Simultaneous Stochastic Optimization of Mining Complexes and Mineral Value Chains. Mathematical Geosciences, 49(3), 341-360. doi:10.1007/s11004-017-9680-3Graves, S. C. (2010). Uncertainty and Production Planning. Planning Production and Inventories in the Extended Enterprise, 83-101. doi:10.1007/978-1-4419-6485-4_5Grillo, H., Alemany, M. M. E., Ortiz, A., & Fuertes-Miquel, V. S. (2017). Mathematical modelling of the order-promising process for fruit supply chains considering the perishability and subtypes of products. Applied Mathematical Modelling, 49, 255-278. doi:10.1016/j.apm.2017.04.037Guan, Z., & Philpott, A. B. (2011). A multistage stochastic programming model for the New Zealand dairy industry. International Journal of Production Economics, 134(2), 289-299. doi:10.1016/j.ijpe.2009.11.003Guide, V. D. R. (2000). Production planning and control for remanufacturing: industry practice and research needs. Journal of Operations Management, 18(4), 467-483. doi:10.1016/s0272-6963(00)00034-6Gupta, V., & Grossmann, I. E. (2011). Solution strategies for multistage stochastic programming with endogenous uncertainties. Computers & Chemical Engineering, 35(11), 2235-2247. doi:10.1016/j.compchemeng.2010.11.013Gupta, S., and Z. Nan. 2006. “‘Multiperiod Planning of Refinery Operations Under Market Uncertainty,’ AIChE Annual Meeting.” Conference Proceedings.Heckmann, I., Comes, T., & Nickel, S. (2015). A critical review on supply chain risk – Definition, measure and modeling. Omega, 52, 119-132. doi:10.1016/j.omega.2014.10.004Heydari, J., & Ghasemi, M. (2018). A revenue sharing contract for reverse supply chain coordination under stochastic quality of returned products and uncertain remanufacturing capacity. Journal of Cleaner Production, 197, 607-615. doi:10.1016/j.jclepro.2018.06.206Hovelaque, V., Duvaleix-Tréguer, S., & Cordier, J. (2009). Effects of constrained supply and price contracts on agricultural cooperatives. European Journal of Operational Research, 199(3), 769-780. doi:10.1016/j.ejor.2008.08.005Hsieh, S., & Chiang, C.-C. (2001). Manufacturing-to-Sale Planning Model for Fuel Oil Production. The International Journal of Advanced Manufacturing Technology, 18(4), 303-311. doi:10.1007/s001700170070Igarashi, M., de Boer, L., & Fet, A. M. (2013). What is required for greener supplier selection? A literature review and conceptual model development. Journal of Purchasing and Supply Management, 19(4), 247-263. doi:10.1016/j.pursup.2013.06.001Jamshidi, M., & Osanloo, M. (2019). Reliability analysis of production schedule in multi-element deposits under grade-tonnage uncertainty with multi-destinations for the run of mine material. International Journal of Mining Science and Technology, 29(3), 483-489. doi:10.1016/j.ijmst.2018.04.016Jin, X., Hu, S. J., Ni, J., & Xiao, G. (2013). Assembly Strategies for Remanufacturing Systems With Variable Quality Returns. IEEE Transactions on Automation Science and Engineering, 10(1), 76-85. doi:10.1109/tase.2012.2217741Jindal, A., & Sangwan, K. S. (2016). Multi-objective fuzzy mathematical modelling of closed-loop supply chain considering economical and environmental factors. Annals of Operations Research, 257(1-2), 95-120. doi:10.1007/s10479-016-2219-zJohnson, P., G. Evatt, P. Duck, and S. Howell. 2010. “The Derivation and Impact of an Optimal Cut-off Grade Regime Upon Mine Valuations,” Proceedings of the World Congress on Engineering 2010 Vol I.Junior, M. L., & Filho, M. G. (2011). Production planning and control for remanufacturing: literature review and analysis. Production Planning & Control, 23(6), 419-435. doi:10.1080/09537287.2011.561815Kamrad, B., & Ernst, R. (2001). An Economic Model for Evaluating Mining and Manufacturing Ventures with Output Yield Uncertainty. Operations Research, 49(5), 690-699. doi:10.1287/opre.49.5.690.10610Kannegiesser, M., Günther, H.-O., van Beek, P., Grunow, M., & Habla, C. (2008). Value chain management for commodities: a case study from the chemical industry. OR Spectrum, 31(1), 63-93. doi:10.1007/s00291-008-0124-9Karabuk, S. (2008). Production planning under uncertainty in textile manufacturing. Journal of the Operational Research Society, 59(4), 510-520. doi:10.1057/palgrave.jors.2602370Khor, C. S., Elkamel, A., & Douglas, P. L. (2008). Stochastic Refinery Planning with Risk Management. Petroleum Science and Technology, 26(14), 1726-1740. doi:10.1080/10916460701287813Kumral, M. (2004). Genetic algorithms for optimization of a mine system under uncertainty. Production Planning & Control, 15(1), 34-41. doi:10.1080/09537280310001654844Lalmazloumian, M., and K. Y. Wong. 2012. “A Review of Modelling Approaches for Supply Chain Planning Under Uncertainty,” Service Systems and Service Management (ICSSSM), 2012 9th International Conference on, pp. 197.Leiras, A., Ribas, G., Hamacher, S., & Elkamel, A. (2013). Tactical and Operational Planning of Multirefinery Networks under Uncertainty: An Iterative Integration Approach. Industrial & Engineering Chemistry Research, 52(25), 8507-8517. doi:10.1021/ie302835nLiao, H., Deng, Q., & Wang, Y. (2017). Optimal Acquisition and Production Policy for End-of-Life Engineering Machinery Recovering in a Joint Manufacturing/Remanufacturing System under Uncertainties in Procurement and Demand. Sustainability, 9(3), 338. doi:10.3390/su9030338Loomba, A. P. S., & Nakashima, K. (2011). Enhancing value in reverse supply chains by sorting before product recovery. Production Planning & Control, 23(2-3), 205-215. doi:10.1080/09537287.2011.591652Macedo, P. B., Alem, D., Santos, M., Junior, M. L., & Moreno, A. (2015). Hybrid manufacturing and remanufacturing lot-sizing problem with stochastic demand, return, and setup costs. The International Journal of Advanced Manufacturing Technology, 82(5-8), 1241-1257. doi:10.1007/s00170-015-7445-zMartinez, L. 2009. “Why Accounting for Uncertainty and Risk Can Improve Final Decision-Making in Strategic Open Pit Mine Evaluation.” Project Evaluation Conference, Melbourne, pp. 1.Matamoros, M. E. V., & Dimitrakopoulos, R. (2016). Stochastic short-term mine production schedule accounting for fleet allocation, operational considerations and blending restrictions. European Journal of Operational Research, 255(3), 911-921. doi:10.1016/j.ejor.2016.05.050Meredith, J. (1993). Theory Building through Conceptual Methods. International Journal of Operations & Production Management, 13(5), 3-11. doi:10.1108/01443579310028120Miller, W. A., Leung, L. C., Azhar, T. M., & Sargent, S. (1997). Fuzzy production planning model for fresh tomato packing. International Journal of Production Economics, 53(3), 227-238. doi:10.1016/s0925-5273(97)00110-2Mitra, K. (2009). Multiobjective optimization of an industrial grinding operation under uncertainty. Chemical Engineering Science, 64(23), 5043-5056. doi:10.1016/j.ces.2009.08.012Moghaddam, K. S. (2015). Fuzzy multi-objective model for supplier selection and order allocation in reverse logistics systems under supply and demand uncertainty. Expert Systems with Applications, 42(15-16), 6237-6254. doi:10.1016/j.eswa.2015.02.010Mula, J., Peidro, D., Díaz-Madroñero, M., & Vicens, E. (2010). Mathematical programming models for supply chain production and transport planning. European Journal of Operational Research, 204(3), 377-390. doi:10.1016/j.ejor.2009.09.008Mula, J., Peidro, D., & Poler, R. (2010). The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand. International Journal of Production Economics, 128(1), 136-143. doi:10.1016/j.ijpe.2010.06.007MUNDI, I., ALEMANY, M. M. E., BOZA, A., & POLER, R. (2013). A Model-Driven Decision Support System for the Master Planning of Ceramic Supply Chains with Non-uniformity of Finished Goods. Studies in Informatics and Control, 22(2). doi:10.24846/v22i2y201305Mundi, M. I., Alemany, M. M. E., Poler, R., & Fuertes-Miquel, V. S. (2016). Fuzzy sets to model master production effectively in Make to Stock companies with Lack of Homogeneity in the Product. Fuzzy Sets and Systems, 293, 95-112. doi:10.1016/j.fss.2015.06.009Munhoz, J. R., & Morabito, R. (2014). Optimization approaches to support decision making in the production planning of a citrus company: A Brazilian case study. Computers and Electronics in Agriculture, 107, 45-57. doi:10.1016/j.compag.2014.05.016Olivetti, E. A., Gaustad, G. G., Field, F. R., & Kirchain, R. E. (2011). Increasing Secondary and Renewable Material Use: A Chance Constrained Modeling Approach To Manage Feedstock Quality Variation. Environmental Science & Technology, 45(9), 4118-4126. doi:10.1021/es103486sOsmani, A., & Zhang, J. (2013). Stochastic optimization of a multi-feedstock lignocellulosic-based bioethanol supply chain under multiple uncertainties. Energy, 59, 157-172. doi:10.1016/j.energy.2013.07.043Paksoy, T., Pehlivan, N. Y., & Özceylan, E. (2012). Application of fuzzy optimization to a supply chain network design: A case study of an edible vegetable oils manufacturer. Applied Mathematical Modelling, 36(6), 2762-2776. doi:10.1016/j.apm.2011.09.060Pauls-Worm, K. G. J., Hendrix, E. M. T., Haijema, R., & van der Vorst, J. G. A. J. (2014). An MILP approximation for ordering perishable products with non-stationary demand and service level constraints. International Journal of Production Economics, 157, 133-146. doi:10.1016/j.ijpe.2014.07.020Peidro, D., Mula, J., Alemany, M. M. E., & Lario, F.-C. (2012). Fuzzy multi-objective optimisation for master planning in a ceramic supply chain. International Journal of Production Research, 50(11), 3011-3020. doi:10.1080/00207543.2011.588267Peidro, D., Mula, J., Jiménez, M., & del Mar Botella, M. (2010). A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment. European Journal of Operational Research, 205(1), 65-80. doi:10.1016/j.ejor.2009.11.031Peidro, D., Mula, J., Poler, R., & Lario, F.-C. (2008). Quantitative models for supply chain planning under uncertainty: a review. The International Journal of Advanced Manufacturing Technology, 43(3-4), 400-420. doi:10.1007/s00170-008-1715-yPendharkar, P. C. (1997). A fuzzy linear programming model for production planning in coal mines. Computers & Operations Research, 24(12), 1141-1149. doi:10.1016/s0305-0548(97)00024-5Pendharkar, P. C. (2013). Scatter search based interactive multi-criteria optimization of fuzzy objectives for coal production planning. Engineering Applications of Artificial Intelligence, 26(5-6), 1503-1511. doi:10.1016/j.engappai.2013.01.001Pieter van Donk, D. (2000). Customer‐driven manufacturing in the food processing industry. British Food Journal, 102(10), 739-747. doi:10.1108/00070700010362176Pitty, S. S., Li, W., Adhitya, A., Srinivasan, R., & Karimi, I. A. (2008). Decision support for integrated refinery supply chains. Computers & Chemical Engineering, 32(11), 2767-2786. doi:10.1016/j.compchemeng.2007.11.006Poles, R., and F. Cheong. 2009. “A System Dynamics Model for Reducing Uncertainty in Remanufacturing Systems,” PACIS 2009–13th Pacific Asia Conference on Information Systems: IT Services in a Global Environment.Pongsakdi, A., Rangsunvigit, P., Siemanond, K., & Bagajewicz, M. J. (2006). Financial risk management in the planning of refinery operations. International Journal of Production Economics, 103(1), 64-86. doi:10.1016/j.ijpe.2005.04.007Radulescu, M., G. Zbaganu, and C. Z. Radulescu. 2008. “Crop Planning in the Presence of Production Quotas (Invited Paper),” Computer Modeling and Simulation, 2008.UKSIM 2008. Tenth International Conference on, pp. 549.Rajaram, K., & Karmarkar, U. S. (2002). Product Cycling With Uncertain Yields: Analysis and Application to the Process Industry. Operations Research, 50(4), 680-691. doi:10.1287/opre.50.4.680.2867Ramasesh, R. V., &

Modeling and Optimal Operation of Hydraulic, Wind and Photovoltaic Power Generation Systems

The transition to 100% renewable energy in the future is one of the most important ways of achieving "carbon peaking and carbon neutrality" and of reducing the adverse effects of climate change. In this process, the safe, stable and economical operation of renewable energy generation systems, represented by hydro-, wind and solar power, is particularly important, and has naturally become a key concern for researchers and engineers. Therefore, this book focuses on the fundamental and applied research on the modeling, control, monitoring and diagnosis of renewable energy generation systems, especially hydropower energy systems, and aims to provide some theoretical reference for researchers, power generation departments or government agencies

New approaches for the real-time optimization of process systems under uncertainty

In the process industry, the economical operation of systems is of utmost importance for stakeholders to remain competitive. Moreover, economic incentives can be used to drive the development of sustainable processes, which must be deployed to ensure continued human and ecological welfare. In the process systems engineering paradigm, model predictive control (MPC) and real-time optimization (RTO) are methods used to achieve operational optimality; however, both methods are subject to uncertainty, which can adversely affect their performance. Along with the challenges of uncertainty, formulations of economic optimization problems are largely problem-specific as process utilities and products vary significantly by application; thus, many nascent processes have not received a tailored economic optimization treatment. In this thesis, the focus is on avenues of economic optimization under uncertainty, namely, the two-step RTO method, which updates process models via parameters; and the modifier adaptation (MA) method, which updates process models via error and gradient correction. In the case of parametric model uncertainty, the two-step RTO method is used. The parameter estimation (PE) step that accompanies RTO requires plant measurements that are often noisy, which can cause the propagation of noise to the parameter estimates and result in poor RTO performance. In the present work, a noise-abatement scheme is proposed such that high-fidelity parameter estimates are used to update a process model for economic optimization. This is achieved through parameter estimate bootstrapping to compute bounds and determine the measurement-set that results in the lowest parameter variation; thus, the scheme is dubbed low-variance parameter estimation (lv-PE). This method is shown to result in improved process economics through truer set points and reduced dynamic behaviour. In the case of structural model mismatch (i.e., unmodelled phenomena), the MA approach is used, whereby gradient modifier (i.e., correction) terms must be recursively estimated until convergence. These modifier terms require plant perturbations to be performed, which incite time-consuming plant dynamics that delay operating point updates. In cases with frequent disturbances, MA may have poor performance well as there is limited time to refine the modifiers. Herein, a partial modifier adaptation (pMA) method is proposed, which selects a subset of modifications to be made, thus reducing the number of necessary perturbations. Through this reduced experimental burden, the operating point refinement process is accelerated resulting in quicker convergence to advantageous operating points. Additionally, constraint satisfaction during this refinement process can also result in poor performance via wasted below-specification products. Accordingly, the pMA method also includes an adjustment step that can drive the system to constraint-satisfying regions at each iteration. The pMA method is shown to economically outperform both the standard MA method as well as a related directional MA method in cases with frequent periodic disturbances. The economic optimization methods described above are implemented in novel processes to improve their economics, which can incite further technological uptake. Post-combustion carbon capture (PCC) is the most advanced carbon capture technology as it has been investigated extensively. PCC takes industrial flue gases and separates the carbon dioxide for later repurposing or storage. Most PCC operating schemes make decisions using simplified models since a mechanistic PCC model is large and difficult to solve. To this end, this thesis provides the first robust MPC that can address uncertainty in PCC with a mechanistic model. The advantage of the mechanistic model in robust optimal control is that it allows for a precise treatment of uncertainties in phenomenological parameters. Using the multi-scenario approach, discrete realizations of the uncertain parameters inside a given uncertainty region can be incorporated into the controller to produce control actions that result in a robust operation in closed-loop. In the case of jointly uncertainty activity coefficients and flue gas flowrates, the proposed robust MPC is shown to lead to improved performance with respect to a nominal controller (i.e., one that does not hedge against uncertainty) under various operational scenarios. In addition to the PCC robust control problem, the mechanistic model is used for economic optimization and state estimation via RTO and moving horizon estimation (MHE) layers respectively. While the former computes economical set points, the latter uses few measurements to compute the full system state, which is necessary for the controller that uses a mechanistic model. These layers are integrated to operate the system economically via a new economic function that accounts for the most significant economic aspects of PCC, including the carbon economy, energy, chemical, and utility costs. A new proposed MPC layer is novel in its ability to enable flexible control of the plant by manipulating fresh material streams to impact CO2 capture and the MHE layer is the first to provide accurate system estimates to the controller with realistically accessible measurements. A joint MPC-MHE-RTO scheme is deployed for PCC, which is shown to lead to more economical steady-state operation compared to constant set point counterfactuals under cofiring, diurnal operation, and price variation scenarios. The lv-PE scheme is also deployed for the PCC system where it is found to improve set point economics with respect to traditional PE methods. The improvements are observed to occur through reduced emissions and more efficient energy used, thus having environmental co-benefits. Moreover, the lv-PE algorithm is used for uncertainty quantification to develop a robust RTO that leads to more conservative set points (i.e., less economic improvement) but lower set point variation (i.e., less control burden). The methodologies developed in this PhD thesis provide improvements in efficacy as well as applicability of online economic optimization in engineering applications, where uncertainty is often present. These can be deployed by both academic as well as industrial practitioners that wish to improve the economic performance on their processes

Multi-scale modelling and optimisation of sustainable chemical processes

This dissertation explores the process modelling and optimisation of chemical processes under sustainability criteria. Resting on process systems engineering techniques combined with life cycle assessment (LCA), we present implementation strategies to improve flowsheet performance and reduce environmental impacts from early design stages. We first address the relevance of sustainability assessments in the sector and present process and environmental modelling techniques available. Under the observation that chemical processes are subject to market, technical, and environmental fluctuations, we next present an approach to account for these uncertainties. Process optimisation is then tackled by combining surrogate modelling, objective-reduction, and multi-criteria decision analysis tools. The framework proved the enhancement of the assessments by reducing the use of computational resources and allowing the ranking of optimal alternatives based on the concept of efficiency. We finally introduce a scheme to assess sustainable performance at a multi-scale level, from catalysis development to planet implications. This approach aims to provide insights about the role of catalysis and establish priorities for process development, while also introducing absolute sustainability metrics via the concept of ‘Planetary boundaries’. Ultimately, this allows a clear view of the impact that a process incurs in the current and future status of the Earth. The capabilities of the methods developed are tested in relevant applications that address challenges in the sector to attain sustainable performance. We present how concepts like circular economy, waste valorisation, and renewable raw materials can certainly bring benefits to the industry compared to their fossil-based alternatives. However, we also show that the development of new processes and technologies is very likely to shift environmental impacts from one category to another, concluding that cross-sectorial cooperation will become essential to meet sustainability targets, such as those determined by the Sustainable Development Goals.Open Acces

Groundwater Management Optimization and Saltwater Intrusion Mitigation under Uncertainty

Groundwater is valuable to supply fresh water to the public, industries, agriculture, etc. However, excessive pumping has caused groundwater storage degradation, water quality deterioration and saltwater intrusion problems. Reliable groundwater flow and solute transport modeling is needed for sustainable groundwater management and aquifer remediation design. However, challenges exist because of highly complex subsurface environments, computationally intensive groundwater models as well as inevitable uncertainties. The first research goal is to explore conjunctive use of feasible hydraulic control approaches for groundwater management and aquifer remediation. Water budget analysis is conducted to understand how groundwater withdrawals affect water levels. A mixed integer multi-objective optimization model is constructed to derive optimal freshwater pumping strategies and investigate how to promote the optimality through regulating pumping locations. A solute transport model for the Baton Rouge multi-aquifer system is developed to assess saltwater encroachment under current condition. Potential saltwater scavenging approach is proposed to mitigate the salinization issue in the Baton Rouge area. The second research goal aims to develop robust surrogate-assisted simulation-optimization modeling methods for saltwater intrusion mitigation. Machine learning based surrogate models (response surface regression model, artificial neural network and support vector machine) were developed to replace a complex high-fidelity solute transport model for predicting saltwater intrusion. Two different methods including Bayesian model averaging and Bayesian set pair analysis are used to construct ensemble surrogates and quantify model prediction uncertainties. Besides. different optimization models that incorporate multiple ensemble surrogates are formulated to obtain optimal saltwater scavenging strategies. Chance-constrained programming is used to account for model selection uncertainty in probabilistic nonlinear concentration constraints. The results show that conjunctive use of hydraulic control approaches would be effective to mitigate saltwater intrusion but needs decades. Machine learning based ensemble surrogates can build accurate models with high computing efficiency, and hence save great efforts in groundwater remediation design. Including model selection uncertainty through multimodel inference and model averaging provides more reliable remediation strategies compared with the single-surrogate assisted approach

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Methodologies for performance enhancement in decentralized supply chains

Ph.DDOCTOR OF PHILOSOPH

A multiscale strategy for fouling prediction and mitigation in gas turbines

Gas turbines are one of the primary sources of power for both aerospace and land-based applications. Precisely for this reason, they are often forced to operate in harsh environmental conditions, which involve the occurrence of particle ingestion by the engine. The main implications of this problem are often underestimated. The particulate in the airflow ingested by the machine can deposit or erode its internal surfaces, and lead to the variation of their aerodynamic geometry, entailing performance degradation and, possibly, a reduction in engine life. This issue affects the compressor and the turbine section and can occur for either land-based or aeronautical turbines. For the former, the problem can be mitigated (but not eliminated) by installing filtration systems. For what concern the aerospace field, filtration systems cannot be used. Volcanic eruptions and sand dust storms can send particulate to aircraft cruising altitudes. Also, aircraft operating in remote locations or low altitudes can be subjected to particle ingestion, especially in desert environments. The aim of this work is to propose different methodologies capable to mitigate the effects of fouling or predicting the performance degradation that it generates. For this purpose, both hot and cold engine sections are considered. Concerning the turbine section, new design guidelines are presented. This is because, for this specific component, the time scales of failure events due to hot deposition can be of the order of minutes, which makes any predictive model inapplicable. In this respect, design optimization techniques were applied to find the best HPT vane geometry that is less sensitive to the fouling phenomena. After that, machine learning methods were adopted to obtain a design map that can be useful in the first steps of the design phase. Moreover, after a numerical uncertainty quantification analysis, it was demonstrated that a deterministic optimization is not sufficient to face highly aleatory phenomena such as fouling. This suggests the use of robust or aggressive design techniques to front this issue. On the other hand, with respect to the compressor section, the research was mainly focused on the building of a predictive maintenance tool. This is because the time scales of failure events due to cold deposition are longer than the ones for the hot section, hence the main challenge for this component is the optimization of the washing schedule. As reported in the previous sections, there are several studies in the literature focused on this issue, but almost all of them are data-based instead of physics-based. The innovative strategy proposed here is a mixture between physics-based and data-based methodologies. In particular, a reduced-order model has been developed to predict the behaviour of the whole engine as the degradation proceeds. For this purpose, a gas path code that uses the components’ characteristic maps has been created to simulate the gas turbine. A map variation technique has been used to take into account the fouling effects on each engine component. Particularly, fouling coefficients as a function of the engine architecture, its operating conditions, and the contaminant characteristics have been created. For this purpose, both experimental and computational results have been used. Specifically for the latter, efforts have been done to develop a new numerical deposition/detachment model.Le turbine a gas sono una delle pricipali fonti di energia, sia per applicazioni aeronautiche che terrestri. Proprio per questa ragione, esse sono spesso costrette ad operare in ambienti non propriamente puliti, il che comporta l’ingestione di contaminanti solidi da parte del motore. Le principali implicazioni di questo problema sono spesso sottovalutate. Le particelle solide presenti nel flusso d’aria che il motore ingerisce durante il suo funzionamento possono depositarsi o erodere le superfici interne della macchina, e portare a variazioni alla sua aerodinamica, quindi a degrado di performance e, molto probabilmente, alla diminuzione della sua vita utile. Questo problema aflligge sia la parte del compressore che la parte della turbina, e si manifesta sia in applicazioni terrestri che aeronautiche. Per quanto riguarda la prima, la questione può essere mitigata (ma non eliminata) dall’installazione di sistemi di filtraggio all’ingresso della macchina. Per le applicazioni aeronautiche invece, i sistemi di filtraggio non possono essere utilizzati. Questo implica che il particolato presente ad alte quote, magari grazie ad eventi catastrofici quali eruzioni vulcaniche, o a basse quote, quindi ambienti deseritic, entra liberamente nella turbina a gas. Lo scopo principale di questo lavoro di tesi, è quello di proporre differenti metodologieallo scopo di mitigare gli effetti dello sporcamento o predirre il degrado che esso comporta nelle turbine a gas. Per questo scopo, sia la parte del compressore che quella della turbina sono state prese in considerazione. Per quanto riguarda la parte turbina, saranno presentate nuove guide progettuali volte al trovare la geometria che sia meno sensibile possibile al problema dello sporcamento. Dopo di ciò, i risultati ottenuti verranno trattati tramite tecniche di machine learning, ottenendo una mappa di progetto che potrà essere utile nelle prime fasi della progettazione di questi componenti. Inoltre, essendo l’analisi fin qui condotta di tipo deterministico, un’analisi delle principali fonti di incertezza verrà eseguita con l’utilizzo di tecniche derivanti dall’uncertainty quantification. Questo dimostrerà che l’analisi deterministica è troppo semplificativa, e che sarebbe opportuno spingersi verso una progettazione robusta per affrontare questa tipologia di problemi. D’altro canto, per quanto concerne la parte compressore, la ricerca è stata incentrata principalmente sulla costruzione di uno strumento predittivo, questo perchè la scala temporale del degrado dovuto alla deposizione a "freddo" è molto più dilatata rispetto a quella della sezione "calda". La trategia proposta in questo lavoro di tesi è un’insieme di modelli fisici e data-driven. In particolare, si è sviluppato un modello ad ordine ridotto per la previsione del comportamento del motore soggetto a degrado dovuto all’ingestione di particolato, durante un’intera missione aerea. Per farlo, si è generato un codice cosiddetto gas-path, che modella i singoli componenti della macchina attraverso le loro mappe caratteristiche. Quest’ultime vengono modificate, a seguito della deposizione, attraverso opportuni coefficienti di degrado. Tali coefficienti devono essere adeguatamente stimati per avere una corretta previsione degli eventi, e per fare ciò verrà proposta una strategia che comporta l’utilizzo sia di metodi sperimentali che computazionali, per la generazione di un algoritmo che avrà lo scopo di fornire come output questi coefficienti