A systematic methodology to critically assess and screen among early stage design alternatives was developed for the use of glycerol. Through deterministic sensitivity analysis it was found that variations in the product and feedstock prices, total production cost, fixed capital investment, and discount rate, among others, have high impact on the project’s profitability analysis. Therefore, the profitability was tested under uncertainties by using NPV and MSP as economic metrics. The robust ranking of solutions is presented with respect to minimum economic risk of the project being nonprofitable (failure to achieve a positive NPV times the consequential profit loss). It was found that the best potential options for glycerol valorization is through the the production of either (i) lactic acid (9 MM$ economic risk with 63% probability of failure to achieve a positive NPV); (ii) succinic acid (14 MM$ economic risk with 76% probability of failure to achieve a positive NPV); or finally, (iii) 1,2-propanediol (16 MM$ economic risk with 68% probability of failure to achieve a positive NPV). As a risk reduction strategy, a multiproduct biorefinery is suggested which is capable of switching between the production of lactic acid and succinic acid. This solution comes with increased capital investment; however, it leads to more robust NPV and decreased economic risk by approximately 20%, therefore creating a production plant that can continuously adapt to market forces and thus optimize profitability.

This paper presents the development of a computer-aided decision support tool for identifying optimal biorefinery concepts for production of biofuels at an early design stage. To this end, a framework that uses a superstructure-based process synthesis approach integrated with uncertainty analysis is used. We demonstrate the application of the tool for generating optimal biorefinery concepts for a lignocellulosic biorefinery. In particular, we highlight the management of various sources of data, the superstructure (integrated thermochemical and biochemical conversion routes) needed to represent the design space, generic but simple models describing the processing tasks, and the formulation and solution of an MINLP problem under deterministic and stochastic conditions to identify the optimal processing route for multiple raw materials and products. Furthermore, we evaluate the impact of market price uncertainties on the optimal solutions and calculate the associated risk to enable informed and risk-aware decisions.

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In this paper, eight optimal biorefi nery concepts for biofuels and biochemicals production are critically analyzed and compared in terms of their techno-economic performance and associated economic risks against historical market fl uctuations. The investigated biorefi nery concepts consider different combinations of biomass feedstock (lignocellulosic versus algal) and conversion technologies (biochemical versus thermochemical). In addition, the economic performance of each biorefi nery concept is tested assuming a sudden drop in oil prices in order to compare the fi tness/survival of each concept under extreme market disturbances. The analyses reveal amongst others that: (i) lignocellulosic bioethanol production is not economically feasible considering a drop in oil prices (a negative internal rate of return); (ii) a multi-product biorefi nery concept, where bioethanol is upgraded to higher value-added chemicals (diethyl ether and 1,3-butadiene), provides an improved resilience and robustness against market price fl uctuations by reducing economic loss by 140 MM$/a (17% IRR); (iii) the economic analysis favors biochemical conversion technologies for a small production/processing capacity, whereas the thermochemical conversion platform is favored for a relatively larger production capacity; and (iv) the microalgae-based biorefi nery concept performed worse in terms of economics compared to the others, which is largely due to the cost of algae production and harvesting. In general, we recommend that a comprehensive economic risk analysis, using for example the Monte Carlo technique, should be an integral part of the conceptual design, development, and optimization of biorefi neries to help improve their economic robustness in view of the competitive market for chemicals and fuels.

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