(483f) Multi-Objective Optimization of Flexible Integrated Biorefinery Design
AIChE Annual Meeting
Wednesday, November 16, 2022 - 2:00pm to 2:18pm
Many sources of uncertainty could have different impacts on biorefinery design and operations. Uncertain parameters including biomass feedstock composition, supply,7product demand, and price8 could be measured on-time to allow recourse actions.9 On the other hand, uncertainty introduced due to reaction kinetics and the difference between lab experiments and scaled-up plant performance can only be estimated rather than measured directly.10, 11 Hence, it is important to incorporate uncertainties in biorefinery design optimization to allow for additional flexibility.12 Flexibility index problem is commonly used to measure systemâs ability to ensure feasible operation over the uncertainty range, which is defined as finding hyperrectangular feasible operating envelopes.11 Another commonly used technique, stochastic programming, samples probability distributions of uncertain parameters and optimizes the expected values of the objective functions.13 Bhosekar et al. applied the two-stage stochastic programming formulation for a biorefinery superstructure optimization to illustrate the effects of uncertainties on the trade-of between process profit and emission.14
A successful biorefinery design is expected to strike a balance between high profit, low emission, and sufficient flexibility. In this work, we aim to develop a methodology for designing such an integrated biorefinery by combining two-stage stochastic programming and flexibility index optimization. A variety of process flowsheets that use biomass feedstocks to synthesize different chemicals are developed in Aspen Plus using experimental and literature data.15 The raw material usage, chemical production, cost, and emission of each flowsheet are extracted to define the economic and environmental objective functions. The Îµ-constraint method is utilized to convert flexibility index and global warming potential objectives into the model constraints.14 The technology choices and plant capacities are decided in the first stage of the stochastic optimization.16 Since multiple plant configurations may have the same flexibility index, direct enumeration of all alternatives during the design stage is impossible. Hence, a surrogate model of the biorefinery's flexibility index is built as a function of plant capacities under reaction conversion, supply, and demand uncertainties. The uncertainty probability distributions of price, supply, and demand are then introduced in the second stage of stochastic programming. Finally, the operation-level decisions representing the optimal flow rates of raw material and intermediates for each biomass conversion unit are chosen to maximize the expected profit and minimize the expected greenhouse gas emission.
- Ögmundarson, Ó.; Herrgård, M. J.; Forster, J.; Hauschild, M. Z.; Fantke, P., Addressing environmental sustainability of biochemicals. Nat. Sustainability 2020, 3 (3), 167-174.
- Athaley, A.; Annam, P.; Saha, B.; Ierapetritou, M., Techno-economic and life cycle analysis of different types of hydrolysis process for the production of p-Xylene. Comput. Chem. Eng. 2019, 121, 685-695.
- Han, J.; Sen, S. M.; Alonso, D. M.; Dumesic, J. A.; Maravelias, C. T., A strategy for the simultaneous catalytic conversion of hemicellulose and cellulose from lignocellulosic biomass to liquid transportation fuels. Green Chem. 2014, 16 (2), 653-661.
- Liao, Y.; Koelewijn, S.-F.; Van den Bossche, G.; Van Aelst, J.; Van den Bosch, S.; Renders, T.; Navare, K.; NicolaÃ¯, T.; Van Aelst, K.; Maesen, M.; Matsushima, H.; Thevelein, J.; Van Acker, K.; Lagrain, B.; Verboekend, D.; Sels, B. F., A sustainable wood biorefinery for lowâcarbon footprint chemicals production. Science 2020, 367 (6484), 1385-1390.
- Kokossis, A. C.; Tsakalova, M.; Pyrgakis, K., Design of integrated biorefineries. Comput. Chem. Eng. 2015, 81, 40-56.
- Gong, J.; You, F., Optimal Design and Synthesis of Algal Biorefinery Processes for Biological Carbon Sequestration and Utilization with Zero Direct Greenhouse Gas Emissions: MINLP Model and Global Optimization Algorithm. Ind. Eng. Chem. Res. 2014, 53 (4), 1563-1579.
- Gulcan, B.; Eksioglu, S. D.; Song, Y.; Roni, M.; Chen, Q., Optimization models for integrated biorefinery operations. Optimization Letters 2021.
- Noh, N. M.; Bahar, A.; Zainuddin, Z. M., Scenario Based Two-Stage Stochastic Programming Approach for the Midterm Production Planning of Oil Refinery. MATEMATIKA: Malaysian Journal of Industrial and Applied Mathematics 2018, 34 (3), 45-55.
- Ostrovsky, G. M.; Datskov, I. V.; Achenie, L. E. K.; Volin, Y. M., Process uncertainty: Case of insufficient process data at the operation stage. AlChE J. 2003, 49 (5), 1216-1232.
- Yue, D.; You, F., Optimal supply chain design and operations under multi-scale uncertainties: Nested stochastic robust optimization modeling framework and solution algorithm. AlChE J. 2016, 62 (9), 3041-3055.
- Ochoa, M. P.; García-Muñoz, S.; Stamatis, S.; Grossmann, I. E., Novel flexibility index formulations for the selection of the operating range within a design space. Comput. Chem. Eng. 2021, 149, 107284.
- Sahinidis, N. V., Optimization under uncertainty: state-of-the-art and opportunities. Comput. Chem. Eng. 2004, 28 (6), 971-983.
- Martín, M.; Martínez, A., Addressing Uncertainty in Formulated Products and Process Design. Ind. Eng. Chem. Res. 2015, 54 (22), 5990-6001.
- Bhosekar, A.; Athaley, A.; Ierapetritou, M., Multiobjective modular biorefinery configuration under uncertainty. Ind. Eng. Chem. Res. 2021, 60 (35), 12956-12969.
- Ibarra-Gonzalez, P.; Rong, B.-G., Integrated Methodology for the Optimal Synthesis of Lignocellulosic Biomass-to-Liquid Fuel Production Processes: 1. Simulation-Based Superstructure Synthesis and Development. Ind. Eng. Chem. Res. 2020.
- Gebreslassie, B. H.; Yao, Y.; You, F., Design under uncertainty of hydrocarbon biorefinery supply chains: Multiobjective stochastic programming models, decomposition algorithm, and a Comparison between CVaR and downside risk. AlChE J. 2012, 58 (7), 2155-2179.