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This article addresses the life cycle optimization (LCO) of the poultry litter supply chain considering pyrolysis technologies that aim to sustainably convert poultry waste into biofuel and biochar...

Many metal–organic framework (MOF) materials have been reported in the literature as promising for carbon capture applications based on isotherm data or simple adsorbent metrics. However, adsorption process conditions are often neglected in these evaluations. In this study, we performed process-level simulation and optimization of pressure swing adsorption processes on a set of promising MOFs reported in the literature for post-combustion carbon capture. Zeolite 13X was also included as a benchmark material. We examined the ability of the MOFs to achieve the Department of Energy goals of 90% CO2 purity and 90% CO2 recovery by employing process-level optimization using three different cycle configurations: a modified Skarstrom cycle, a five-step cycle, and a fractionated vacuum swing adsorption cycle. Then, we ranked the MOFs based on their economic performance by looking at the productivity and energy requirements for each cycle. We compared this ranking of the MOFs with the rankings provided by other metrics and found that the adsorbent rankings suggested by simplified metrics may differ significantly from the rankings predicted by detailed process optimization. The economic optimization analysis suggests that the best performing MOFs from those analyzed here are UTSA-16, Cu-TDPAT, Zn-MOF-74, Ti-MIL-91, and SIFSIX-3-Ni. Looking at the connection between process performance and material properties, we found that high CO2 working capacity, small pore size, and large difference between the heat of adsorption of CO2 and N2 promote CO2 capture ability based on this small data set. We synthesized one of the top performing MOFs, SIFSIX-3-Ni, and measured its CO2 and N2 adsorption isotherms. The measured isotherms allowed us to estimate the N2 heat of adsorption for SIFSIX-3-Ni, which was not previously available and was required for the process-level modelling.

Dairy manure is an avoidable waste largely raising public concerns on the environment and human health issues. However, from the opposite perspective, it can be viewed as a steady-supplied feedstock with both high quantity and high nutrient contents and can be used to produce several value-added products, especially biochar and energy. To comprehensively compare the environmental impacts resulted from dairy manure treatment using four potential thermochemical conversion technologies: slow pyrolysis, fast pyrolysis, gasification, and hydrothermal liquefaction. In this study, we adopt two different life cycle assessment approaches, namely, attributional LCA (ALCA) and consequential LCA (CLCA). Unlike ALCA which only assesses environmental impacts directly related to the target process, CLCA also captures indirect environmental impacts in consequence of changes in the supply and demand through a simple partial equilibrium model by adopting microeconomic concepts. In both ALCA and CLCA results, slow pyrolysis outperforms the other technologies due to the splendid carbon sequestration ability of the biochar it produces.

In this work we provide a perspective on Process Systems Engineering (PSE) in the context of sustainability, reviewing the main tools available and describing major applications in sustainability problems spanning multiple scales, from molecules, through chemical plants, and finally the enterprise and macroeconomic levels. After highlighting the potential role of PSE in meeting the UN Sustainable Development Goals, we discuss future research directions, focusing on major modelling and algorithmic challenges along with the trend to explore new application domains beyond chemical engineering while still revisiting problems within the core discipline.

Safe, efficient, and sustainable operations and control are primary objectives in industrial manufacturing processes. State-of-the-art technologies heavily rely on human intervention, thereby showing apparent limitations in practice. The burgeoning era of big data is influencing the process industries tremendously, providing unprecedented opportunities to achieve smart manufacturing. This kind of manufacturing requires machines to not only be capable of relieving humans from intensive physical work, but also be effective in taking on intellectual labor and even producing innovations on their own. To attain this goal, data analytics and machine learning are indispensable. In this paper, we review recent advances in data analytics and machine learning applied to the monitoring, control, and optimization of industrial processes, paying particular attention to the interpretability and functionality of machine learning models. By analyzing the gap between practical requirements and the current research status, promising future research directions are identified. Keywords: Big data, Machine learning, Smart manufacturing, Process systems engineering

Description, Sustainability of reusing Li-ion batteries is hindered by high nickel content and is susceptible to use and recycling choices., Second life and recycling of retired automotive lithium-ion batteries (LIBs) have drawn growing attention, as large volumes of LIBs will retire in the coming decade. Here, we illustrate how battery chemistry, use, and recycling can influence the energy and environmental sustainability of LIBs. We find that LIBs with higher specific energy show better life cycle environmental performances, but their environmental benefits from second life application are less pronounced. Direct cathode recycling is found to be the most effective in reducing life cycle environmental impacts, while hydrometallurgical recycling provides limited sustainability benefits for high-performance LIBs. Battery design with less aluminum and alternative anode materials, such as silicon-based anode, could enable more sustainable LIB recycling. Compared to directly recycling LIBs after their electric vehicle use, carbon footprint and energy use of LIBs recycled after their second life can be reduced by 8 to 17% and 2 to 6%, respectively.

The worldwide plastic waste accumulation has posed probably irreversible harm to the environment, and the main dilemma for this global issue is: How to define the waste quality grading system to maximise plastic recyclability? This work reports a machine learning approach to evaluating the recyclability of plastic waste by categorising the quality trends of the contained polymers with auxiliary materials. The result reveals the hierarchical resource quality grades predictors that restrict the mapping of the waste sources to the demands. The Pinch Analysis framework is then applied using the quality clusters to maximise plastic recyclability. The method identifies a Pinch Point – the ideal waste quality level that limits the plastic recycling rate in the system. The novel concept is applied to a problem with different polymer types and properties. The results show the maximum recycling rate for the case study to be 38 % for PET, 100 % for PE and 92 % for PP based on the optimal number of clusters identified. Trends of environmental impacts with different plastic recyclability and footprints of recycled plastic are also compared.

Plastic waste and its environmental hazards have been attracting public attention as a global sustainability issue. This study builds a neural network model to forecast plastic waste generation of the EU-27 in 2030 and evaluates how the interventions could mitigate the adverse impact of plastic waste on the environment. The black-box model is interpreted using SHapley Additive exPlanations (SHAP) for managerial insights. The dependence on predictors (i.e., energy consumption, circular material use rate, economic complexity index, population, and real gross domestic product) and their interactions are discussed. The projected plastic waste generation of the EU-27 is estimated to reach 17 Mt/y in 2030. With an EU targeted recycling rate (55%) in 2030, the environmental impacts would still be higher than in 2018, especially global warming potential and plastic marine pollution. This result highlights the importance of plastic waste reduction, especially for the clustering algorithm-based grouped countries with a high amount of untreated plastic waste per capita. Compared to the other assessed scenarios, Scenario 4 with waste reduction (50% recycling, 47.6% energy recovery, 2.4% landfill) shows the lowest impact in acidification, eutrophication, marine aquatic toxicity, plastic marine pollution, and abiotic depletion. However, the global warming potential (8.78 Gt CO2eq) is higher than that in 2018, while Scenario 3 (55% recycling, 42.6% energy recovery, 2.4% landfill) is better in this aspect than Scenario 4. This comprehensive analysis provides pertinent insights into policy interventions towards environmental hazard mitigation.

The objective of this study was to compare the application of iterative linear programming (iteLP), sequential quadratic programming (SQP), and mixed-integer nonlinear programming-based deterministic global optimization (MINLP_DGO) on ration formulation for dairy cattle based on Nutrient Requirements of Dairy Cattle (NRC, 2001). Least-cost diets were formulated for lactating cows, dry cows, and heifers. Nutrient requirements including energy, protein, and minerals, along with other limitations on dry matter intake, neutral detergent fiber, and fat were considered as constraints. Five hundred simulations were conducted, with each simulation randomly selecting 3 roughages and 5 concentrates from the feed table in NRC (2001) as the feed resource for each of 3 animal groups. Among the 500 simulations for lactating cows, 57, 45, and 21 simulations did not yield a feasible solution when using iteLP, SQP, and MINLP_DGO, respectively. All the simulations for dry cows and heifers were feasible when using SQP and MINLP_DGO, but 49 and 11 infeasible simulations occurred when using iteLP for dry cows and heifers, respectively. The average ration costs per animal per day of the feasible solutions obtained by iteLP, SQP, and MINLP_DGO were $4.78 (±0.71), $4.45 (±0.65), and $4.44 (±0.65) for lactating cows; $2.39 (±0.52), $1.48 (±0.26), and $1.48 (±0.26) for dry cows; and $0.98 (±0.72), $0.97 (±0.15), and $0.91 (±0.14) for heifers, respectively. The average computation time of iteLP, SQP, and MINLP_DGO were 0.59 (±1.87) s, 1.15 (±0.62) s, and 58.69 (±68.45) s for lactating cows; 0.041 (±0.070) s, 0.76 (±0.37) s, and 14.84 (±39.09) s for dry cows; and 1.60 (±2.90) s, 0.51 (±0.19) s, and 16.45 (±45.56) s for heifers, respectively. In conclusion, iteLP had limited capability of formulating least-cost diets when nonlinearity existed in the constraints. Both SQP and MINLP_DGO handled the nonlinear constraints well, with SQP being faster, whereas MINLP_DGO was able to return a feasible solution under some situations where SQP could not.

CO2 capture is a promising way of offloading the impact of fossil fuels on global warming. Although various techniques have been proposed for CO2 capture, the main obstacle remains the economic per...

Carbon capture technologies are expected to play a key role in the global energy system, as it is likely that fossil fuels will continue to be dominant in the world’s energy mix in the near future. Pressure swing adsorption (PSA) is a promising alternative among currently available technologies for carbon capture due to its low energy requirements. Still, the design of the appropriate PSA cycle for a given adsorbent material is a challenge that must be addressed to make PSA commercially competitive for carbon capture applications. In this work, we propose and test a model reduction-based approach that systematically generates low-order representations of rigorous PSA models. These reduced-order models are obtained by training artificial neural networks on data collected from full partial differential algebraic equation (PDAE) model simulations. The main contribution of this paper is the development of surrogate models for every possible step in PSA cycles: pressurization, adsorption, and depressurization ...

This article addresses the optimal design of carbon-neutral hybrid energy system with deep water source cooling, biomass heating, and geothermal heat and power. A novel superstructure of the proposed hybrid energy system comprised of an enhanced geothermal system, a torrefied biomass-based combustion system, and a deep water source cooling system with conventional chillers as auxiliaries, is developed. Based on the superstructure of the proposed hybrid energy system, we develop a multi-period optimization model to minimize a fractional metric, the levelized cost. Because the main product of the hybrid energy system is heat, the levelized cost is expressed as levelized cost of heat, with other byproducts indirectly incorporated by using credits. The resulting nonconvex mixed-integer nonlinear fractional programming problem is efficiently solved by using a tailored optimization algorithm. Two case studies based on Cornell’s campus in Ithaca, New York are presented to quantify the effect of different electric power sources on the technoeconomic objective, as well as the life cycle greenhouse gas emissions. The first case study considers electric power from natural gas, while a carbon-neutral electric power supply based on renewable geothermal energy is envisioned in the second case study. Results show that switching the electric power supply from natural gas to geothermal energy could reduce the greenhouse gas emissions by 24.5%, while only increasing the levelized cost of heat by 5.6%. The carbon footprint for both case studies are promisingly low, compared with numerous existing heat generation technologies. Through sensitivity analysis, the project lifetime is identified as the most influential input parameter.

Energy optimization of utility system helps to reduce the operating cost and save energy for the industrial plants. Widespread uncertainties such as device efficiency and process demand pose new challenges for this issue. A hybrid modeling framework is presented by introducing the operating data into mechanism model to adapt the changes of device efficiency and operating conditions. Mathematical models of boilers, steam turbines, and letdown valves are then developed in the framework. Based on the process historical data of a real-world plant, a Dirichlet process mixture model is used to capture the support information of uncertain parameters. Bridging data-driven robust optimization (DDRO) and utility system optimization under uncertainty, a robust mixed-integer nonlinear programming (MINLP) model is developed by utilizing the derived uncertainty set. The robust counterpart of the developed model can be reformulated as a tractable MINLP problem including conic quadratic constraints that could be solved efficiently. A real-world case study is carried out to demonstrate the effectiveness of the proposed approach in protecting against uncertainties and achieving a good trade-off between optimality and robustness of the operational decisions for industrial utility systems.

The Food-Energy-Water-Waste Nexus (FEWWN) represents the interconnections between food, energy, water, and waste production systems, and it has become a key research area. Enormous quantities of agricultural and organic wastes are produced throughout the FEWWN. Often, these wastes are not treated appropriately because their true costs are rarely quantified, and usually externalized to the environment. This shortcoming is addressed from a systems perspective fused with approaches from ecological economics. A regional bioenergy production model where bioenergy may be produced from ethanol and/or agricultural wastes is constructed. Ecosystem service valuation methods are integrated into the framework, allowing for bioenergy production systems to be designed to minimize ecological damage and/or maximize ecological restoration. These values are captured within a Green Gross Domestic Product (Green GDP) objective that values both energy produced and ecosystem service values lost/gained. System profit is another objective in the multi-objective model. The framework is applied to a bioenergy production system for the U.S. state of New York, which aims to produce 10% more bioenergy compared to its current levels. Net changes in Green GDP ranged from -$16.5 M/y to $90.6 M/y, and corresponding profits ranged from $7.2 M/y to -$74.5 M/y. Corn grain ethanol was the dominant source of bioenergy in solutions with higher profits, while ethanol from corn stover and bioelectricity generated from animal manure biogas contributed more bioenergy in solutions with increasing Green GDP. Results show that there is a trade-off between promoting natural capital/ecological health and financial profit. FEWWN system design should consider these trade-offs moving forward.

A biomass product and process network that displays how organic waste and other non-traditional biomass feedstocks may be converted into useful bioproducts and biofuels is a necessary addition to the field of biomass conversion and utilization. We develop a processing network of 216 technologies and 172 materials/compounds that contains conversion pathways of agricultural and organic waste biomass sources, such as food peels, animal manure, and grease. To examine the effectiveness and economic feasibility of these conversion pathways, the biomass product and process network is optimized for return on investment. The resulting problem is a data-driven two-stage adaptive robust mixed-integer nonlinear fractional program, which was effectively solved via a tailored optimization algorithm. The proposed approach is applied to two case studies in which traditional agricultural feedstocks are used alongside biological and agricultural waste feedstocks. The selected feedstocks were used to satisfy and, in some cases, even exceed demand for selected products. The optimal pathways have returns on investment of 26.1% and 6.2%, with utilized conversion technologies ranging from hydrocracking to microwave hydrodiffusion. In both cases, we find that profitable processing pathways are utilized at maximum capacities to increase return on investment. Specifically, in the case study where orange peel wastes are used to produce pectin, we find that this pathway is highly profitable at the given market price. The two cases that are run using the proposed model are then compared to additional cases to display differences that arise when uncertainty is not considered and the objective function of the model is changed.

Geothermal energy has significant potential to reduce fossil fuel consumptions and environmental impacts. To improve energy conversion efficiency of geothermal energy systems, numerous systems designs have been proposed and their optimization sought. At this point, it is worth reviewing current developed geothermal energy systems because understanding configurations and principles of basic and state-of-the-art technologies is important for developing advanced energy systems. A comprehensive review of the geothermal energy systems is carried out from the perspective of systems analysis, design, and optimization. Results illustrate that limited sets of parameters have been considered in most studies on design and optimization, though these studies provide great insight into specific designs. However, all influential factors have to be fully considered for practical applications. This study identifies and organizes influential factors for geothermal energy systems. In addition, critical analyses of studies on systems design and optimization are performed to determine limitations of current studies. As polygeneration systems produce various energy products (electricity, heat, and/or cooling), it might play key roles to maximize utilization of geothermal energy. Especially, polygeneration systems with binary technology, which can effectively produce electricity from moderate temperature geothermal resources, have significant potentials to enhance the overall performance. In this regard, the energy production strategy and technology selection are of significant importance to meet electric, heating, and cooling loads efficiently. To fill the knowledge gaps and to maximize geothermal energy utilization, this review proposes state-of-the-art multi-scale modeling and optimization framework.

This paper proposes a novel data-driven adaptive robust optimization (ARO) framework for the unit commitment (UC) problem integrating wind power into smart grids. By leveraging a Dirichlet process mixture model, a data-driven uncertainty set for wind power forecast errors is constructed as a union of several basic uncertainty sets. Therefore, the proposed uncertainty set can flexibly capture a compact region of uncertainty in a nonparametric fashion. Based on this uncertainty set and wind power forecasts, a data-driven adaptive robust UC problem is then formulated as a four-level optimization problem. A decomposition-based algorithm is further developed. Compared to conventional robust UC models, the proposed approach does not presume single mode, symmetry, or independence in uncertainty. Moreover, it not only substantially withstands wind power forecast errors, but also significantly mitigates the conservatism issue by reducing operational costs. We also compare the proposed approach with the state-of-the-art data-driven ARO method based on principal component analysis and kernel smoothing to assess its performance. The effectiveness of the proposed approach is demonstrated with the six-bus and IEEE 118-bus systems. Computational results show that the proposed approach scales gracefully with problem size and generates solutions that are more cost effective than the existing data-driven ARO method.

Recovering the remaining cold energy from the regasification process is one of the key challenges of the overall LNG value chain. This paper aims to develop a cryogenic energy storage system (CES) integrated with LNG direct expansion regasification (LNG–CES) that can recover cold energy and store it as cryogenic energy using air as the working fluid. Cold energy of LNG is available in two forms: thermal energy by heat exchange and shaft work by expansion, while the cryogenic storage process requires compression and cooling. The supply and demand of LNG direct expansion and cryogenic energy storage processes are well balanced. Therefore, a combined LNG–CES process to store energy will prove efficient. This study proposes an industrial-feasible design for the LNG–CES process and energy optimization to maximize net power output from the process. Moreover, a novel process design is proposed to recover cold energy lost during LNG regasification more efficiently. Energy optimization results of the proposed design demonstrated an 11.04% increase in the net power generation from the feasible configuration of the base design. Additionally, the cause of this improvement was studied using thermodynamic analyses.

Liquefied natural gas (LNG) demand has been rapidly increasing due to the global need for clean energy resources. This study analyzes and compares LNG regasification processes and technologies from...

This paper investigates the influences of uncertainty in multi-stakeholder non-cooperative supply chains, and the corresponding optimal strategies based on game theory to hedge against uncertainty in design and operations of such decentralized supply chains. We propose a novel game-theory-based stochastic model that integrates two-stage stochastic programming with a single-leader-multiple-follower Stackelberg game scheme for optimizing decentralized supply chains under uncertainty. Both the leader's and the followers’ uncertainties are considered, which directly affect their design and operational decisions regarding infrastructure development, contracts selection, price setting, production profile, transportation planning, and inventory management. The resulting model is formulated as a two-stage stochastic mixed-integer bilevel nonlinear program, which can be further reformulated into a tractable single-level stochastic mixed-integer linear program by applying KKT conditions and Glover's linearization method. An illustrative example of flight booking under uncertain flight delays and a large-scale application to shale gas supply chains are presented to demonstrate the applicability of the proposed framework.

This paper analyzes and compares the life cycle environmental impacts of two major types of Li-ion batteries using process-based and integrated hybrid life-cycle assessment (LCA) approaches. The life cycle inventories (LCIs) of Li-ion battery contain component production, battery assembly, use phase, disposal and recycling and other related background processes. For process-based LCA, 17 ReCiPe midpoint environmental impact indicators and three end point environmental impact indicators are considered. As for the integrated hybrid LCA study, life cycle greenhouse gas (GHG) emissions and energy consumption are emphasized. Furthermore, we perform sensitivity analysis of life cycle GHG emissions with respect to the uncertainties in product prices, mass of BMS and cooling system, and production efficiency. The integrated hybrid LCA results show that battery cell production is the most significant contributor to the life cycle GHG emissions and the economic input-output (EIO) systems contribute the largest part...

Cover crop is an agriculture operation that is planted during the winter and owns several advantages such as improving water quality and soil quality. However, the large-scale effect of cover crop in relieving environmental burden and improving cash crop yield over a region has not been widely investigated. Due to cost and time limitation, it is not favorable to conduct the conventional field trials. Previous study proposed a Random Forest classifier to predict the pattern of cover crop adoption from the remote sensing data. In this study, we propose a Multilayer Perceptron neural network to further improve the performance and reliability of the classification model and achieve an accuracy of 0.93 and Cohen’s Kappa of 0.76. Moreover, the Multilayer Perceptron model outperforms two baseline classification models. Finally, we predict the cover crop planting status for the Knox County and found a significant increase in cover crop planting on the corn cropland in 2016.

The chemical industry is facing ever-increasing challenges for developing novel products and processes capable of reducing environmental impacts and curbing resource depletion. Yet, the interplay between molecular phenomena and the design of products and processes are often oversimplified. Machine learning stands uniquely positioned to disentangle the complexity of multiscale modeling by leveraging data to navigate the design spaces of multifaceted molecular systems. Herein, we limit our survey of machine learning applications in computational molecular design (CMD) to four elements: property estimation, catalysis, synthesis planning, and design methods. Through this perspective, we aim to offer a roadmap for future work on multiscale modeling that better explores the interplay between nanoscale features and macroscale decisions in product and process design.