Probabilistic multi-criteria analysis for evaluation of biodiesel production technologies from used cooking oil

Multi-criteria decision analysis (MCDA) is a widely adopted tool, which is regarded as a suitable set of methods to evaluate sustainability in a variety of energy applications. The MCDA is usually performed employing a deterministic approach. Such an approach is rather not adequate to include model uncertainties connected with both: decision maker preferences (criteria weights) and process input data. By considering the stochastic nature of uncertainties, it is possible to generate results that can easily be analyzed statistically. In addition, such an approach allows investigating the consequences of the resulting uncertainty of multi-attribute aggregation on the final decision. In this study, we define, analyze and compare four different technologies of biodiesel production from used cooking oil (UCO) using probabilistic MCDA. Among the criteria, we consider energy, economic, environmental, as well as social aspects. To evaluate the uncertainty of the final results, we propose a novel method, coupling Monte Carlo simulation and method of data reconciliation. Firstly, we present the results of the deterministic case, in which we assume the exact values of criteria and the preferences. Besides, we investigate on a sensitivity of the results to identify the major factors affecting the economic, energy, environmental and social viability of biodiesel production. Furthermore, we conduct an analysis of the contributions of the different phases of the UCO processing to the resulting criteria. Finally, we introduce inaccurate or uncertain criteria values and criteria preferences to evaluate the uncertainty of the results. (C) 2017 Elsevier Ltd. All rights reserved.