466913 Global Sensitivity Analysis of Economic Assessment of Early Stage Process Design: The Case of the Glycerol Biorefinery

Wednesday, November 16, 2016: 4:21 PM
Carmel I (Hotel Nikko San Francisco)
Gürkan Sin, Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark, Carina Gargalo, Department of Chemical and Biochemical Engineering, DTU, Kgs. Lyngby, Denmark, Ana Isabel Carvalho, The Department of Engineering and Management, Instituto Superior Técnico, Lisboa, Portugal and Krist V. Gernaey, CAPEC-PROCESS Research Center, Dept. of Chemical and Biochemical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark

Nowadays it has become progressively important to achieve a sustainable process performance in order to keep a viable and competitive advantage in the international markets. Thus, the development of all-inclusive/holistic/comprehensive and systematic methods to accomplish this goal is the topic of this study. To this end, a multi-level framework for early-stage design and screening of process options is proposed for techno-economic and environmental sustainability analysis through risk assessment. The alternatives composing the design space are assessed by following the framework’s steps, targeting to (i) quantify the economic risk; (ii) quantify the potential environmental risk under uncertainties by using monetary valuation of environmental impacts techniques; (iii) measure the concepts’ eco-efficiency; and, finally (iv) facilitate the decision-making procedure by interpreting the trade-offs through a qualitative and quantitative sustainability risk assessment matrix. The framework application is highlighted through the critical assessment and screening of early stage glycerol-based biorefinery design concepts. It was found that the best potential options for glycerol valorisation is are obtained through the production of either: (i) lactic acid; (ii) succinic acid; or, (iii) 1,2-propanediol. To reduce the potential economic risk, a multi-product biorefinery is suggested, whose strategy is based on its capability of switching between the production of lactic acid and succinic acid.

The risk assessment tool described above provide valuable information on how uncertainties (technical versus external such as feed/product prices) impact the decision of optimal process concept and how to better mitigate these impacts by improving technology development. In this contribution, we take one step further and ask the question, a given a certain economic risk associated with a conceptual process alternative, what is the contribution of input uncertainties((e.g. feed composition, yield, feedstock prices, product prices, etc)) to the risk? If risk is defined as variance (deviation from an expected value), what is the individual variance attributed to each input uncertainty? And how we can use this information to further minimize economic risk and improve technology advancement? To answer these questions, we use sensitivity analysis methods including local methods such as one-factor-at-a-time (OAT) as well as global methods such as variance decomposition to compute first order as well as total sensitivity indices. We implemented algorithms in Matlab (R2015a) based on Monte Carlo sampling using Latin Hypercube Sampling with Iman Conover correlation control (Sin et al 2009) and sampling based on conditional probability of dependent variables proposed by Kucherenko et al 2012 to take into account the correlation structure of input uncertainties (especially price correlation matrix). The economic model used in this work is the discounted cash-flow rate of return (DCFR). The OAT sensitivity analysis has shown that deviations in the product’s and feedstock prices, total production cost, fixed capital investment as well as discount rate, among others, have a high impact on the project’s profitability (quantified with NPV and MSPA global sensitivity analysis of NPV indicated two major factors contributing to its variance (risk): uncertainty in fixed capital cost estimation (82%), and, uncertainty in product prices (14%). The NPV meta-model showed that improving the accuracy of fixed capital cost estimation (5% std) leads to a 95% reduction in the NPV’s uncertainty. This global sensitivity analysis confirms the process engineering expectation that more detailed capital cost estimation supported through pilot scale studies or more rigorous model analysis is expected to decrease the economic risk and better judge the transferability of technology to the market. Global sensitivity analysis is an important and complementary tool to study and decompose impact of uncertainties to economic assessment of conceptual process design studies.

Keywords: techno-economic assessment, uncertainty analysis, glycerol-based biorefinery concepts, risk-based decision making, global sensitivity analysis.


Kucherenko, S., Tarantola, S., & Annoni, P. (2012). Estimation of global sensitivity indices for models with dependent variables. Computer Physics Communications183(4), 937-946.

Sin, G., Gernaey, K. V., & Lantz, A. E. (2009). Good modeling practice for PAT applications: Propagation of input uncertainty and sensitivity analysis. Biotechnology progress25(4), 1043-1053.

Cheali, P., Gargalo, C., Gernaey, K. V., & Sin, G. (2015). A Framework for Sustainable Design of Algal Biorefineries: Economic Aspects and Life Cycle Analysis. In Algal Biorefineries (pp. 511-535). Springer International Publishing.

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