FIEMA, a system of fuzzy inference and emission analytics for sustainability-oriented chemical process design

In the quest to achieve sustainable development goals, developments in sustainability-oriented chemical process design are key to innovation in the chemical industry, especially important for processes aiming for sustainable fuels. One of the greatest challenges is the difficulty of modeling the highly complex interactions among the design variables, such as catalyst technology attributes, and greenhouse gas emissions. Most of the computational aids crucial to deal with the complexity of chemical processes require data that is either unavailable or uncertain at an early stage of design. The multistage integrated system for sustainable design proposed in this paper boosts these computational aids by applying data science techniques to allow uncertainty to be handled more efficiently, thereby facilitating the modeling of the interactions between the properties of new materials or processes and sustainability indicators. In this system, current data connectivity methods are used to find paths of correlation among catalysts properties and greenhouse gas emissions. The key feature of the proposed system relies on the integration through multiple stages of Fuzzy Inference systems and a data-driven technique for Emissions Analytics, FIEMA.1 The algorithm in FIEMA provides a semi-supervised learning approach to emission analytics: it determines data clusters by a C-means algorithm and subsequently builds fuzzy sets for multiple stages of input-output inference. The proposed FIEMA system was demonstrated in an effort to determine the optimal configurations of the properties of catalysts to minimize the probability of associated greenhouse gas emissions for a methanol production process. The results showed the potential of this approach to reduce the search space of catalyst material designs by suggesting promising configurations for oxygen storage capacity, mechanical strength, lifetime, size, and poisoning level. The research impacts of this study contribute to the development of clean fuels by a computationally-efficient system for early design, and by the determination of catalysts development paths that assure an actual reduction of the life-cycle emissions.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

Innovation in sustainability-oriented chemical process design is crucial for achieving sustainable development goals, especially in the production of sustainable fuels. However, the complex interactions among design variables and the lack of early-stage data pose significant challenges. This paper proposes a multistage integrated system that utilizes data science techniques to improve computational aids and enable efficient handling of uncertainty. By applying current data connectivity methods, the system can model the correlations between catalyst properties and greenhouse gas emissions. The proposed system combines Fuzzy Inference systems and a data-driven technique for Emissions Analytics to determine optimal catalyst configurations for minimizing emissions. The research impacts of this study contribute to the development of clean fuels by providing a computationally-efficient system for early design and determining catalyst development paths that reduce life-cycle emissions.