Sustainability encompasses many wicked problems involving complex interdependencies across social, natural, and engineered systems. We argue holistic multiscale modeling and decision-support frameworks are needed to address multifaceted interdisciplinary aspects of these wicked problems. This review highlights three emerging research areas for artificial intelligence (AI) and machine learning (ML) in molecular-to-systems engineering for sustainability: (1) molecular discovery and materials design, (2) automation and self-driving laboratories, (3) process and systems-of-systems optimization. Recent advances in AI and ML are highlighted in four contemporary application areas in chemical engineering design: (1) equitable energy systems, (2) decarbonizing the power sector, (3) circular economies for critical materials, and (4) next-generation heating and cooling. These examples illustrate how AI and ML enable more sophisticated interdisciplinary multiscale models, faster optimization algor... [more]

Following the discovery of the least squares method in 1805 by Legendre and later in 1809 by Gauss, surrogate modeling and machine learning have come a long way. From identifying patterns and trends in process data to predictive modeling, optimization, fault detection, reaction network discovery, and process operations, machine learning became an integral part of all aspects of process design and process systems engineering. This is enabled, at the same time necessitated, by the vast amounts of data that are readily available from processes, increased digitalization, automation, increasing computation power, and simulation software that can model complex phenomena that span over several temporal and spatial scales. Although this paper is not a comprehensive review, it gives an overview of the recent history of machine learning models that we use every day and how they shaped process design problems from the recent advances to the exploration of their prospects.

Sustainability of the chemical and materials industry (CMI) requires it to achieve net-zero emis-sions of greenhouse gases and other resources while making decisions that have a net-positive impact on nature and society. Many corporations, nations, and universities have pledged to meet such goals but systematic models, methods, and tools to guide this transition are missing. We pre-sent a framework to meet this need. It involves developing a comprehensive, open access model of the global CMI. In addition to existing technologies, this model includes emerging alternatives for renewable energy, circularization, and carbon capture, utilization and storage. Systematic methods help identify innovation opportunities and develop roadmaps that account for long-term changes such as technology evolution and climate change. Meeting the goal of net-zero emis-sions requires inclusion of life cycle impacts. Nature-positive decisions need to encourage eco-logical protection and restoration. Thi... [more]

In this work, we consider the thermo-mechanical exergy of a substance for cold applications, even as it approaches absolute zero. This is relevant for cold-service applications such as refrigeration, liquefied natural gas, air separation, and liquid hydrogen. We demonstrate how the optimization formulation for the determination of exergy is the most suitable way for process systems engineers to think about exergy. We provide an illustrative example by computing thermo-mechanical exergy of neon approaching absolute zero. We also discuss how this result relates with the Third Law of Thermodynamics, both how it is used to compute thermo-mechanical exergy, but also what it implies about the validity of the results and the equations used to compute them.

This work investigates the water impact of carbon capture technologies employed in coal and natural gas power generation, viz. integrated gasification combined cycle, oxy-fuel combustion, solid oxide fuel cells and post-combustion solvent-based. The Water Impact per CO2 Avoided (WICa) metric was developed to understand the tradeoff between water usage and global warming potential, and additionally as a decision-making tool. It relates the impact on available water resources to greenhouse gas reduction over the cradle-to-plant-exit lifecycle by leveraging existing metrics, including the Water Impact Index (WII), water withdrawal, water consumption, water quality, and Water Scarcity Index (WSI). The results show that some carbon capture technologies increase the overall water usage of power generation plants, thereby increasing the water impact per CO2 avoided. Solid oxide fuel cells and oxy-fuel technology, though not mature in comparison to post-combustion capture, have the least water... [more]

This visual presentation introduces the concept of exergy to a chemical process systems engineering audience, such as how to compute it for heat and for substances at various pressures and temperatures. The presentation also goes over seven examples of how exergy analysis can be used in process design education, such as in utility or capital cost estimation, heat integration, direct air capture, power production, carbon dioxide capture and compression, and pinch analysis.