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Industrial networks consist of multiple industrial nodes interacting with each other through material exchanges that support the overall production goal of the network.

 

Dynamical equations form the basis of design for manufacturing processes and control systems; however, identifying governing equations using a mechanistic approach is tedious. Recently, Machine learning (ML) has shown promise to identify the governing dynamical equations for physical systems faster. This possibility of rapid identification of governing equations provides an exciting opportunity for advancing dynamical systems modeling. However, applicability of the ML approach in identifying governing mechanisms for the dynamics of complex systems relevant to manufacturing has not been tested. We test and compare the efficacy of two white-box ML approaches (SINDy and SymReg) for predicting dynamics and structure of dynamical equations for overall dynamics in a distillation column. Results demonstrate that a combination of ML approaches should be used to identify a full range of equations. In terms of physical law, few terms were interpretable as related to Fick’s law of diffusion and Henry’s law in SINDy, whereas SymReg identified energy balance as driving dynamics. 

Sustainable transition to low carbon and zero waste economy requires a macroscopic evaluation of opportunities and impact of adopting emerging technologies in a region. However, a full assessment of current physical flows and wastes is a tedious task, thus leading to lack of comprehensive assessment before scale up and adoption of emerging technologies. Utilizing the mechanistic models developed for engineering and biological systems with macroeconomic framework of Input-Output models, we propose a novel integrated approach to fully map the physical economy, that automates the process of mapping industrial flows and wastes in a region. The approach is demonstrated by mapping the agro-based physical economy of the state of Illinois, USA by using mechanistic models for 10 sectors, which have high impact on waste generation. Each model mechanistically simulates the material transformation processes in the economic sector and provides the material flow information for mapping. The model for physical economy developed in the form of a Physical Input-Output Table (PIOT) captures the interindustry physical interactions in the region and waste flows, thus providing insights into the opportunities to implement circular economy strategies i.e., adoption of recycling technologies at large scale. In Illinois, adoption of technologies for industrial waste-water & hog manure recycling will have the highest impact by reducing > 62 % of hog industry waste, > 99 % of soybean hull waste, and > 96 % of dry corn milling (corn ethanol production) waste reduction. Small % reduction in fertilizer manufacturing waste was also observed. The physical economy model revealed that Urea sector had the highest material use of 5.52E+08 tons and green bean farming with lowest material use of 1.30E+05 tons for the year modeled (2018). The mechanistic modeling also allowed to capture elemental flows across the physical economy with Urea sector using 8.25E+07 tons of carbon per operation-year (highest) and bean farming using 3.90E+04 tons of elemental carbon per operation-year (least). The approach proposed here establishes a connection between engineering and physical economy modeling community for standardizing the mapping of physical economy that can provide insights for successfully transitioning to a low carbon and zero waste circular economy. 

Amidst the growing population, urbanization, globalization, and economic growth, along with the impacts of climate change, decision-makers, stakeholders, and researchers need tools for better assessment and communication of the highly interconnected food–energy–water (FEW) nexus. This study aimed to identify critical periods for water resources management for robust decision-making for water resources management at the nexus. Using a 4610 ha agricultural watershed as a pilot site, historical data (2006–2012), scientific literature values, and SWAT model simulations were utilized to map out critical periods throughout the growing season of corn and soybeans. The results indicate that soil water deficits are primarily seen in June and July, with average deficits and surpluses ranging from −134.7 to +145.3 mm during the study period. Corresponding water quality impacts include average monthly surface nitrate-N, subsurface nitrate-N, and soluble phosphorus losses of up to 0.026, 0.26, and 0.0013 kg/ha, respectively, over the growing season. Estimated fuel requirements for the agricultural practices ranged from 24.7 to 170.3 L/ha, while estimated carbon emissions ranged from 0.3 to 2.7 kg CO2/L. A composite look at all the FEW nexus elements showed that critical periods for water management in the study watershed occurred in the early and late season—primarily related to water quality—and mid-season, related to water quantity. This suggests the need to adapt agricultural and other management practices across the growing season in line with the respective water management needs. The FEW nexus assessment methodologies developed in this study provide a framework in which spatial, temporal, and literature data can be implemented for improved water resources management in other areas. 

Life Cycle Analysis (LCA) has long been utilized for decision making about the sustainability of products. LCA provides information about the total emissions generated for a given functional unit of a product, which is utilized by industries or consumers for comparing two products with regards to environmental performance. However, many existing LCAs utilize data that is representative of an average system with regards to life cycle stage, thus providing an aggregate picture. It has been shown that regional variation may lead to large variation in the environmental impacts of a product, specifically dealing with energy consumption, related emissions and resource consumptions. Hence, improving the reliability of LCA results for decision making with regards to environmental performance needs regional models to be incorporated for building a life cycle inventory that is representative of the origin of products from a certain region. In this work, we present the integration of regionalized data from process systems models and other sources to build regional LCA models and quantify the spatial variations per unit of biodiesel produced in the state of Indiana for environmental impact. In order to include regional variation, we have incorporated information about plant capacity for producing biodiesel from North and Central Indiana. The LCA model built is a cradle-to-gate. Once the region-specific models are built, the data were utilized in SimaPro to integrate with upstream processes to perform a life cycle impact assessment (LCIA). We report the results per liter of biodiesel from northern and central Indiana facilities in this work. The impact categories studied were global warming potential (kg CO2 eq) and freshwater eutrophication (kg P eq). While there were a lot of variations at individual county level, both regions had a similar global warming potential impact and the northern region had relatively lower eutrophication impacts.