Integrated Methodology for the Optimal Synthesis of Lignocellulosic Biomass-to-Liquid Fuel Production Processes: 1. Simulation-Based Superstructure Synthesis and Development

Ibarra-Gonzalez, Paola; Rong, Ben-Guang

doi:10.1021/acs.iecr.0c02902

Cited by 5 publications

(5 citation statements)

References 47 publications

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“…Conversion processes can be based on both biochemical or thermochemical routes. Generally, thermochemical processes are widely used for biomass conversion and energy recovery since they use well-developed technologies [1][2][3][4]. On the other side, biomass-derived carbohydrates represent a versatile platform for the production of a variety of final products [5].…”

Section: Introductionmentioning

confidence: 99%

Arundo donax Refining to Second Generation Bioethanol and Furfural

et al. 2020

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Biomass-derived sugars are platform molecules that can be converted into a variety of final products. Non-food, lignocellulosic feedstocks, such as agroforest residues and low inputs, high yield crops, are attractive bioresources for the production of second-generation sugars. Biorefining schemes based on the use of versatile technologies that operate at mild conditions contribute to the sustainability of the bio-based products. The present work describes the conversion of giant reed (Arundo donax), a non-food crop, to ethanol and furfural (FA). A sulphuric-acid-catalyzed steam explosion was used for the biomass pretreatment and fractionation. A hybrid process was optimized for the hydrolysis and fermentation (HSSF) of C6 sugars at high gravity conditions consisting of a biomass pre-liquefaction followed by simultaneous saccharification and fermentation with a step-wise temperature program and multiple inoculations. Hemicellulose derived xylose was dehydrated to furfural on the solid acid catalyst in biphasic media irradiated by microwave energy. The results indicate that the optimized HSSF process produced ethanol titers in the range 43–51 g/L depending on the enzymatic dosage, about 13–21 g/L higher than unoptimized conditions. An optimal liquefaction time before saccharification and fermentation tests (SSF) was 10 h by using 34 filter paper unit (FPU)/g glucan of Cellic® CTec3. C5 streams yielded 33.5% FA of the theoretical value after 10 min of microwave heating at 157 °C and a catalyst concentration of 14 meq per g of xylose.

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Section: Introductionmentioning

confidence: 99%

Arundo donax Refining to Second Generation Bioethanol and Furfural

et al. 2020

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“…The process flowsheets of each candidate conversion technology could be developed in Aspen Plus based on experimental data or literature . The input and output flow rates, operating cost, and capital investment of each block are extracted and scaled to formulate the optimization objectives and constraints.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The ANN model with ReLU activation function is able to achieve excellent approximation while retaining the mixed-linear characteristic of the model, which is well-suited for the biorefinery multiobjective optimization problem. 38 The input and output flow rates, operating cost, and capital investment of each block are extracted and scaled to formulate the optimization objectives and constraints. The optimization framework could also include other promising biomass conversion routes and unit operations that are not modeled in the Aspen Plus.…”

Section: Introductionmentioning

confidence: 99%

Multifeedstock and Multiproduct Process Design Using Neural Network Surrogate Flexibility Constraints

Luo

Ierapetritou

2023

Ind. Eng. Chem. Res.

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Biorefineries are designed to utilize a combination of various technologies to transform biomass derived raw materials into different valueadded products. This strategy has been highlighted in the literature for reducing waste, increasing profitability, and improving the process resilience to uncertain biomass feedstocks. In this work, a two-stage stochastic programming (TSSP) model is developed to maximize profit and minimize emissions under different sources of uncertainties. Data-driven surrogate models are built for biorefinery's flexibility index (FI) to quantify and improve its operational flexibility. The neural network with rectified linear unit (ReLU) activation function is established as the appropriate surrogate model because it closely approximates the flexibility index while retaining the mixed-integer linear characteristics of the overall design formulation. Moreover, the stochastic programming demonstrates the magnitude of environmental impact uncertainty quantitatively in each scenario using empirical price/demand/supply uncertainty information, which cannot be addressed by the traditional Pedigree-based life cycle assessment (LCA) uncertainty analysis.

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“…Deterministic optimization for the renewable source processing network design for different chemical products and energy production have been extensively investigated during the past decades, for example, methane production from water, CO 2 , and renewable sources; bioethanol production via gasification of switchgrass or hydrolysis of lignocellulosic switchgrass; biodiesel production from lignocellulosic switchgrass or cooking oil and algae; hydrogen, liquid fuel, commodity chemical (ethylene, propylene, butylene, benzene, etc.) production from biorenewables via fast pyrolysis; power production from biomass, wind, and solar as well as its storage via hydrogen-based dense energy carriers and battery; hydrogen production from biomass and solar; methanol production from algae, solar, and wind; glycerol production from biomass; polyacrylonitrile fiber and diesel substitute production via algae; biogasoline, biodiesel, and power production from biomass wind, solar, waste, and water; , liquid fuel production from lignocellulosic biomass; − nitrogen-containing polymer production from biomass; isoprene production from biomass; ammonia production from wind power or biomass; and so forth.…”

Section: Introductionmentioning

confidence: 99%

Multistage Distributionally Robust Design of a Renewable Source Processing Network under Uncertainty

Liu

Yuan

2021

Ind. Eng. Chem. Res.

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Integration of renewable sources into chemical and power production is one of the crucial pathways toward an effective, affordable, and deep decarbonization of the world economy in line with carbon neutrality and sustainable development. The present work concentrates on the robust design of the process network for multiproduct and power production with renewable sources under uncertainty. We propose a multistage distributionally robust optimization model to hedge against uncertain chemical product demand and feedstock availability. 1-norm Wasserstein metric is employed to construct the ambiguity set with limited uncertain data. We derive an equivalent robust counterpart of the multistage distributionally robust optimization based on the reformulations of the worst-case expectation term of each stage by taking advantage of a complete recourse, which remedies the computational issues. Computational results, including one base case and four correlated cases with different capacities, demonstrate that the optimal solutions from the proposed multistage distributionally robust optimization with the Wasserstein ambiguity set achieve better out-of-sample performance than the conventional multistage stochastic programming. Obviously, this is because distributionally robust optimization incorporates the worst-case probability distribution, while the multistage stochastic programming only considers empirical distribution.

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Integrated Methodology for the Optimal Synthesis of Lignocellulosic Biomass-to-Liquid Fuel Production Processes: 1. Simulation-Based Superstructure Synthesis and Development

Cited by 5 publications

References 47 publications

Arundo donax Refining to Second Generation Bioethanol and Furfural

Arundo donax Refining to Second Generation Bioethanol and Furfural

Multifeedstock and Multiproduct Process Design Using Neural Network Surrogate Flexibility Constraints

Multistage Distributionally Robust Design of a Renewable Source Processing Network under Uncertainty

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