(483f) Multi-Objective Optimization of Flexible Integrated Biorefinery Design | AIChE

(483f) Multi-Objective Optimization of Flexible Integrated Biorefinery Design


Ierapetritou, M., University of Delaware
The growing concerns over global warming and environmental issues motivate the research on replacing oil-based feedstocks with biomass raw material for chemical and fuel production. Various technologies have been developed, scaled up, and commercialized over the years.1 However, most biomass conversion technologies are optimized to selectively convert a specific feedstock component (e.g., cellulose,2 hemicellulose,3 or lignin4) while the rest becomes the waste stream. Hence, the integrated biorefinery is proposed to combine different conversion technologies and fully utilize all biomass components.5 The superstructure optimization framework with all available alternative reaction routes is commonly used to design the biorefinery.6 In addition to selecting the most economical or sustainable feedstock-technology-product combinations, the integrated biorefinery strategy also gives the process more flexibility to adjust its production in the volatile chemical market.

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 system’s 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 ε-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.


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