(83c) Estimating Environmental Impacts at Early-Stage Process Synthesis Using Machine Learning Approach
AIChE Annual Meeting
Monday, November 14, 2022 - 8:50am to 9:15am
To that end, we present in this work, a ML approach to estimating the environmental pressures of early-stage process networks. In this work, we collect data on chemicals , material flows, environmental releases, and emissions to air, ground and water bodies from databases and repositories such as the EPA TRI (Toxic Release Inventory) Explorer, FRS (Facility Registry Services), RCRA (Resource Conservation and Recovery Act), and the NREL US Life Cycle Inventory Database (âRelease Chemical Report | TRI Explorer | US EPA,â n.d.; US EPA, 2015). From these available datasets, we identify the physicochemical and structural properties such as functional groups, molecular weights, number of atoms, H/C ratios, bond strengths, toxicity, health and exposure impacts of these chemicals. Since environmental impacts are ultimately linked to mass and energy flows of a product, thermodynamic properties add additional information when combined with these molecular descriptors. Hence in this analysis we further consider these thermodynamic properties to increase the accuracy of the predictions. Next, we collect targeted process-related emissions and sustainability metrics data via LCA tools such as GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies) model by Argonne National Lab, SIMAPRO® and its Ecoinvent database, SPIonWeb, GREENSCOPE. The datasets from these tools are used in conjunction with the datasets from the chemical releases databases and repositories to predict new chemical and process metrics including Process Mass Index (PMI), CO2 emissions, Global Warming Potential, Emergy, Disability-Adjusted Lifetime Years (DALY), and Sustainable Process Index (SPI) (Brown et al., 2012; Goedkoop and Spriensma, 2001; Narodoslawsky, 2015; Shahzad et al., 2014; Smith et al., 2019). This predictive correlation is achieved using multi-regression ML algorithms. In our analysis, the functional unit is 1 kg of product chemical. Figure 1 shows the schematic of this approach.
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