Process Design and Optimization of Chemicals’ Production from Biomass Feedstocks
Zhaojia Lin1, Abhay Athaley1, Vladimiros Nikolakis2, Marianthi Ierapetritou1
1 Department of Chemical and Biochemical Engineering, Rutgers - The State University of New Jersey
2. Catalysis Center for Energy Innovation & Department of Chemical & Biomolecular Engineering, University of Delaware
This work focuses on the process design and optimization of processes for the production of chemicals from lignocellulosic biomass feedstocks. The acceptance of these products in the market place depends on their competitiveness compared with petroleum-based products not only in terms of economics but also in terms of process sustainability.6 The development of efficient and economically sustainable routes for the production of bio-based platform chemicals such as 5-hydroxymethylfurfural (HMF), furfural, levulinic acid, etc. has attracted a lot of attention.1,2 We investigate paths based on the non-enzymatic hydrolysis of the cellulosic and hemicellulosic fractions to produce sugar mixtures followed by “one-pot” reactive extraction to convert sugar mixtures to furan derivatives. The aforementioned chemicals can be produced from glucose or xylose solutions using reactive extraction. However, the requirement for the use of organic solvents as extractants and salts that promote the partition coefficients of HMF and furfural have a negative impact on the economics and sustainability. Moreover, xylose and glucose solutions from hydrolysis steps usually contain small amounts of glucose or xylose respectively, unless further purification process is implemented.3 Therefore, it appears more economically and environmentally attractive to simultaneously manufacture HMF and furfural from C5 and C6 mixtures that are produced from concentrated-acid biomass hydrolysis.4,5
Previous studies evaluated the economics and environmental impacts of these bio-products independently;7-9 however only few studies have explored the optimization of biobased production involving both reaction and separation steps.10 It is essential to implement flowsheet optimization to obtain the optimal operating conditions that minimizes production costs and environmental impacts both at the production but also at the separation stage. Surrogate-based optimization is a useful tool for the cases that 1) original models are expensive to evaluate; 2) the original gradient information is inaccessible for those commercial simulation tools or the noise exists so that the gradient-based algorithms cannot be accurately used. 11,12
This work uses techno-economic analysis and life cycle assessment to design and evaluate the production alternatives. The detailed kinetics models are included in the reaction step and the separation units are simulated using Aspen Plus. At the next stage a surrogate-based optimization approach is used to achieve the optimal alternative in terms of economic and environmental criteria. More specifically, the surrogate-based models are generated to represent one or groups of original reaction and separation units; next the surrogate-based models are connected and reformulated into multi-criteria nonlinear programing problem (NLP); and finally NLP problem is solved by a commercial solver to present the pareto optimal set of solutions.
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