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Urban Metabolism (UM) resembles living organisms in a community by consuming necessities and excreting waste. Using a circular economy concept with UM enhances energy and material circulation and closes waste loops. As a campus metabolism case study, this research tracks energy, food waste, and construction materials flows at Cornell University, Ithaca, NY. These sectors are interconnected at the food and energy systems regarding emissions. The study includes environmental and economic evaluations of proposed food waste mitigation and embodied emission estimation of building stocks. The results suggest that transitioning from natural gas to geothermal is a viable renewable option that can increase circularity. Regarding food waste, the proposed digestate composting reduces emissions by 16 to 39 metric tons of CO2e with a financial gain of 90 to 400 kUS$/y compared to traditional composting practices. Lastly, the comparative assessment of operational emissions from the energy sector and embodied emissions from stored building materials reveals a rising trend in the proportion of embodied emissions contribution, from 53.58 % in 2008 to 73.14 % in 2021. To offset the anticipated release of embodied carbon, Cornell requires strategies to manage construction waste and enhance circularity within this sector. To achieve the decarbonization goal, the campus metabolism study advances the understanding of energy and material flows, emissions, and offsets among the three sectors. This study can unlock sustainability pathways and accelerate Cornell’s progress toward carbon neutrality by 2035.

Communities are one of the essential elements of modern cities, significantly contributing to overall energy consumption and carbon emissions. To further support ongoing decarbonization initiatives, this study presents a novel Physically Consistent Deep Learning (PCDL)-based Model Predictive Control (MPC) approach for community energy management. This approach incorporates both building-to-grid interactions and on-site renewable energy resources. The PCDL model, starting with the definition of physics consistency, is constructed in line with the established physical laws that govern community thermal dynamics. Serving as a precise thermal load and indoor climate estimator, the PCDL model is then implemented in a centralized MPC to reduce the community energy cost and maintain comfortable indoor environments for buildings in the community. To verify the effectiveness and control performance of the proposed framework, we use a simulation case study of a student residential hall at Cornell University. The results demonstrate that the PCDL-based MPC is highly effective in maintaining comfortable indoor conditions and contributes to load shifting and shaving through its participation in the demand response service.

This study introduces a novel cyber-physical-biological system for energy management and crop production in plant factories. The goal is to minimize energy consumption while maintaining operational efficiency in sustainable food production. The CPBS integrates various control variables, including temperature, humidity, lighting, and CO2 levels, and accurately captures plant biological dynamics while predicting crop growth within controlled environments. To achieve this, physics-informed deep learning techniques are utilized to develop computationally efficient digital twins of the plant factory's internal microclimate and crop states. By incorporating Artificial Intelligence (AI), the control objectives are fine-tuned to minimize energy usage and resource expenses, ensuring sustainable crop production rates under different daytime scenarios. The results demonstrate an impressive 8.75 % reduction in energy usage compared to alternative control methods, enhancing operational efficiency and promoting sustainability in plant factories. The proposed approach offers a promising solution for achieving sustainable food production with minimized energy consumption in controlled environments.

A novel model predictive control (MPC) framework is proposed to optimize the energy management of integrated rooftop greenhouses and buildings, aiming to reduce control costs and the likelihood of climate violations. The centralized intelligent control approach employed for both the integrated rooftop greenhouse and the building ensures optimal conditions for crops and occupants. The integrated rooftop greenhouse utilizes waste heat and air from the building, resulting in reduced energy and CO2 consumption. The nonlinear dynamic models of temperature, humidity, and CO2 concentration for integrated rooftop greenhouse climate and building are first constructed. An integrated optimization problem is then formulated to acquire the optimal control decisions. The proposed MPC framework is implemented to regulate temperature, humidity, and CO2 level via controlling fans, pad cooling, shades, heating, ventilation and air conditioning systems, CO2 injection, and lighting systems. The indoor climate of an integrated rooftop greenhouse on a building in Brooklyn, New York, is controlled for the case study to show the advantages of the proposed nonlinear model predictive control framework. The results show that the average energy savings from the building to the integrated rooftop greenhouse amount to 15.2 % with the integration of the i-RTG and the host building under the proposed MPC framework.

Plant factories with artificial lighting (PFALs) are heralded as a potential solution to enhance the resilience of the food production system by amplifying productivity per unit area of land. However, PFALs typically demand higher resource consumption than traditional greenhouses or open-field farming. To optimize resource use in PFALs, we developed a nonlinear, automatic control system utilizing deep reinforcement learning (DRL). The proposed system implicitly learns the intricate dynamics of crop behavior and environmental variables, aiming to curtail resource, particularly energy consumption in PFALs, while ensuring that these environmental variables stay within desired operating parameters. We demonstrate the efficacy of the proposed DRL-based automatic control system through a case study: a shipping container PFAL situated in Ithaca, New York. We evaluate the ability of the proposed DRL system to reduce energy consumption by comparing its energy consumption to that of the conventional control method. Our findings indicate that, in typical Ithaca summer and winter conditions, the DRL-based control system can potentially decrease energy consumption by about 31 % and 23 % compared to the conventional control method.

As the growing world population drives demand for poultry meat and eggs, a surplus of poultry manure is produced, which, when applied to land, can lead to freshwater eutrophication and harmful algae blooms. To sustainably manage poultry manure, several waste-to-energy and soil amendment pathways have been developed, drawing from technologies such as pyrolysis, hydrothermal liquefaction, hydrothermal carbonization, and anaerobic digestion. To evaluate the “cradle-to-gate” life cycle impacts of the pathways, as well as how the introduction of these technologies affects the upstream and downstream markets for products, two different Life Cycle Assessments (LCA) methods are used. In an attributional LCA setting, direct land application has the lowest environmental impacts except prominently in the climate change and eutrophication categories, which are the categories for which its harms are most noticeable. In a consequential LCA setting, due to the consideration of offsets, direct land application only has the lowest energy resource use and ionizing radiation, while other technologies minimize all other impact categories. To meet environmental targets, the choice of management technology for poultry manure is significantly determined by the chosen system boundary, implying that careful consideration of environmental objectives is necessary for the circular economy.

Driven by the Paris Agreement for curbing global greenhouse gas (GHG) emissions, extensive research efforts are undertaken in pursuit of a carbon-neutral future. The energy sector is the first to bear the brunt of climate change due to its large share of GHG emissions, and a decarbonization strategy is urgently needed. In compliance with three main lines of Corporate Social Responsibility, this study explores the environmental, economic, and social impacts of hybrid energy systems decarbonization by integrating the environmental sustainability, techno-economic and sociological considerations through a multi-objective optimization framework. The proposed model simultaneously minimizes the total annualized costs, minimizes the scope 1, 2, 3 emissions for environmental justice, and maximizes medium opportunity hours by integrating life cycle assessment and economic input-output analysis. The optimization results provide guidance for sustainable design and operations of energy systems, identify environmental hotspots and associated mitigation strategies, and inform the resulting social benefits. The applicability of the proposed modeling framework is illustrated through case study using Cornell’s main campus.

While the implementation of renewable energy systems and model predictive control (MPC) could reduce non-renewable energy consumption, one challenge to building climate control using MPC is the weather forecast uncertainty. In this work, we propose a data-driven robust model predictive control (DDRMPC) framework to address building climate control with renewable hybrid energy systems under weather forecast uncertainty. The control and energy system configurations include heating, ventilation, air conditioning, geothermal heat pump, photovoltaic panel, and electricity storage battery. Historical weather forecast and measurement data are gathered from the weather station to identify the forecast errors and for the use of uncertainty set construction. The data-driven uncertainty sets are constructed with multiple machine learning techniques, including principal component analysis (PCA) with kernel density estimation (KDE), K-means clustering coupled with PCA and KDE, and Dirichlet process mixture model (DPMM). Lastly, a data-driven robust optimization problem is developed to obtain the optimal control inputs for a building with renewable energy systems. A case study on controlling a building with renewable energy systems located on the Cornell University campus is used to demonstrate the advantages of the proposed DDRMPC framework.

In this study, we develop a novel nonlinear model predictive control (NMPC) framework for climate control of buildings with renewable energy systems to minimize electricity costs. A nonlinear dynamic model of the building climate and renewable energy systems, including temperature, humidity, thermal comfort, geothermal heat pump, and solar panels, is first constructed based on mass and energy balance equations. The nonlinear dynamic model is then integrated into the proposed NMPC framework, which iteratively solves a nonlinear programming problem to generate the optimal control inputs, which minimize energy consumption and carbon footprints for sustainability. A simulation case study on controlling a building located on the Cornell University campus is conducted to demonstrate the capability of renewable energy sources to reduce building energy consumption using the proposed NMPC framework. The results show the NMPC framework could efficiently minimize total electricity cost and constraint violation for thermal comfort to 12.9 % with no more than 0.2 of violation on the predicted mean value index in different seasons. Implementing an electricity storage component could reduce the electricity cost by 19 %. The results indicate better sustainability for the smart building using sustainable energy sources and the NMPC framework.

Various pathways have been adopted for hydrogen production over the years, with technologies like steam methane reforming, often referred to as the 'brown' or 'blue' hydrogen, being the most prevalent. Different colors correspond to different production pathways, each having its life cycle greenhouse gas emissions. Using process simulation results as the major life cycle inventory data, life cycle impact assessment results for brown and blue hydrogen pathways have been presented. The results of the life cycle analysis study were utilized to determine the set of optimum operating conditions, with the global warming potential (GWP) corresponding to the blue hydrogen pathway at the optimal operating conditions being 33 % less than the optimal brown hydrogen counterpart. The operating cost for the brown hydrogen pathway at the optimal operating conditions can be reduced up to 69 % for the studied range of process operating conditions.

The existing COVID-19 pandemic has driven personal protective equipment use and consumption surge, leading to plastic pollution as most waste goes to landfills. Biodegradable polypropylene (PP) gowns claimed to have similar production costs as standard PP film ones might be more environmentally friendly due to the degradability after landfilling. The illustration of their sustainable end-of-life waste disposal options is lacking and requires a systematic comparison of their environmental impacts. A holistic life cycle assessment approach based on full-spectrum environmental indicators identifies the environmentally sustainable waste disposal options. Results illustrate the environmental benefits of landfill gas capture and utilization incorporated to landfilling biodegradable gown wastes by reducing 48.81 % land-use, 9.35 % greenhouse gas emissions from fossil sources, and 5.67 % from land-use greenhouse gas emissions, respectively. Despite these environmental advantages, industrial composting embodies lower environmental emissions than sanitary landfills for treating biodegradable gowns. Fossil-based gowns treated by landfills can have lower environmental impacts than composting biodegradable gowns in full-spectrum environmental indicators. The standard gown landfilling is identified as the environmentally sustainable disposal option.

As Cornell is transitioning to a carbon-free energy system by 2035, the campus energy system of the future will be based on 100% renewable energy sources. Specifically, the electricity will be mainly sourced from the local electric grid, which is expected to be carbon-free in the next two decades. Earth source heating and lake source cooling will serve as the major source for base-load renewable heating and cooling, respectively. Multiple geothermal wells will be drilled to meet the base-load heating demand. A conventional chiller will continue to provide auxiliary cooling sources for hot summer days in addition to the LSC system. Peak load will be fulfilled by introducing thermal energy storage and green hydrogen. This study addresses the economically optimal future design by developing a multi-period optimization model, to provide insights for the campus energy systems transition. A systematic life cycle assessment is adopted to examine the extent of carbon neutrality based on the optimization results.

This paper proposes a novel robust unit commitment (UC) framework with data-driven disjunctive uncertainty sets for volatile wind power outputs, assisted by machine learning techniques. To flexibly identify the uncertainty space based on wind power forecast error data with disjunctive structures, the uncertainty data are grouped using K-means and density-based spatial clustering of applications with noise following the optimal cluster number determined by the Calinski-Harabasz index. The disjunctive uncertainty sets are constructed accordingly as the union of multiple basic uncertainty sets, including conventional box and budget uncertainty sets, and data-driven uncertainty sets using Dirichlet process mixture model, principal component analysis coupled with kernel density estimation, and support vector clustering. The problem is formulated into a two-stage adaptive robust UC model with data-driven disjunctive uncertainty sets and with a multi-level optimization structure. To facilitate the solution process, a tailored decomposition-based optimization algorithm is developed. The effectiveness of the proposed framework is illustrated using an application on the IEEE 39-bus system. The proposed approach can reduce the price of robustness by 8-38 % compared to the conventional “one-set-fits-all” approaches. Benchmarking with stochastic programming indicates that the proposed framework can achieve the same or better economic performance with over 30 % less computational time.

Consequential life cycle assessment (CLCA) enables evaluating the environmental consequences of market dynamics overlooked by attributional life cycle assessment (ALCA). A consequential life cycle optimization (CLCO) framework is developed in this work to determine the economically optimal and most environmentally sustainable chemical recycling pathway for high-density polyethylene waste. The framework includes CLCA and techno-economic analysis (TEA), and the CLCO problem is formulated as a multi-objective mixed-integer nonlinear fractional programming problem (MINFP) that is effectively solved by an optimization algorithm. This multi-objective optimization problem aims to minimize the unit life cycle environmental impacts with maximum unit net present value (NPV). Environmental assessment results show that the total greenhouse gas (GHG) emissions evaluated by the CLCA approach are 14.22 % lower than those assessed by the ALCA approach. By evaluating the ReCiPe end-point score, CLCA further reduces the total environmental impacts corresponding to particulate matter formation by 17.37 %.

Model Predictive Control (MPC) has gained popularity in recent years and is widely adopted in building control. This study proposes a novel data-driven robust MPC to make the optimal heating plan, specifically for the multi-zone single-floor building. In this study, the room temperature and relative humidity (RH) will be highly valued in the optimisation decision. To better incorporate RH into the state-space model (SSM), the linear relations between RH and other room temperature parameters in the thermal zones are formulated, ensuring the better linear fitting of SSM to the original nonlinear model. Afterward, k-means clustered, principal component analysis (PCA), and kernel density estimation (KDE) based data-driven uncertainty set is constructed and applied to MPC. The other three kinds of MPC’s are compared to our proposed data-driven robust MPC (RMPC), including conventional RMPC, k-means clustered, data-driven RMPC (KM-DDRMPC), PCA and KDE based data-driven RMPC (PKDDRMPC). The results demonstrate that the optimality of our proposed k-means clustered, PCA and KDE based data-driven RMPC (KM-PKDDRMPC), which consumes 9.8 % to 17.9 % less energy in controlling both temperature and RH, compared to other data-driven robust MPC’s, and essentially follow the constraints which certainty equivalent MPC and conventional RMPC cannot conform.

As an organic waste with high nutrient contents, the conventional landfill or incineration of poultry waste has led to growing concerns. This study applies spatial analysis to two scenarios of poultry waste valorization supply chain design in New York State (NYS). Ultimately, the optimal distribution of slow and fast pyrolysis biorefineries within NYS based on the operational and economic parameters, locations of major poultry farms, crude refineries and corn croplands is discerned and mapped. Additionally, the biochar transportation costs are distributed in the form of heat maps. The outcome indicates that building a single centralized biorefinery in NYS for both slow pyrolysis and fast pyrolysis is more economically feasible than building multiple smaller biorefineries for ten slow pyrolysis and eight fast pyrolysis distributed biorefineries in NYS.

With increasing concerns on environmental impacts of retired lithium-ion batteries (LIBs) and supply risk of critical materials, second life and recycling are considered as promising strategies to mitigate the environmental impacts of retired automotive LIBs. In this life cycle assessment (LCA) study, we investigate environmental benefits of second life and recycling methods on three types of widely adopted, commercialized automotive LIBs, including lithium manganese oxide (LMO), lithium nickel manganese cobalt oxide (NCM) and lithium nickel cobalt aluminum oxide (NCA). The results show that when subjected to second life, LIBs with both superior energy storage capacity and energy density generate more environmental benefits than LIBs that is less competitive in either working performance (31 % - 44 %, 16 % - 21 % and 50 % - 56 % reduction in environmental impacts). This is in line with the current trend towards the development of electric vehicles with larger energy storage capacity and longer battery life. We also found that direct cathode recycling outperforms hydrometallurgical and pyrometallurgical recycling regardless of battery chemistry. Among all recycling methods, only pyrometallurgical recycling is carbon intensive. The recovery of cathode active material and metals, including copper and aluminum, are critical to the environmental performance of batteries.

This study examines four prominent thermochemical conversion technologies such as slow pyrolysis (SP), fast pyrolysis (FP), gasification (GA) and hydrothermal liquefaction (HTL), for treating poultry litter in New York State (NYS). Nine cases involving various combinations of the four technologies and different downstream processing options such as bio-oil upgrading, Fischer-Tropsch conversion and combined heat and power generation are chosen based on the product composition and distribution. High-fidelity process simulations for each technological platform are performed to derive the mass and energy balance information. Calculations and breakdown of the equipment costs, capital costs, operating and maintenance costs and utilities are provided and compared extensively for each of the nine cases. The economic performance is further analyzed by calculating and comparing the resultant net present values (NPV), ranging from $10 M to $170 M (SP), $89 M to $314.5 M (FP), $28 M to $196 M (HTL) and $25 M to $234 M (GA). The greenhouse gas emission inventories are also compiled to understand the corresponding impacts of different downstream processing choices (ranging from 217 to 494.5 kg CO2-eq/t feedstock with both the pyrolysis technologies outperforming the others in most cases) and to highlight the trade-off with economic performance. Through sensitivity analysis, the influential factors requiring further investigation are identified and it is found that plant capacity and bio-oil yield are the most important parameters for the fast pyrolysis systems, biochar price for SP is the single most important parameter.

This work proposes a novel data-driven robust model predictive control (DDRMPC) framework for automatic control of greenhouse temperature and CO2 concentration level. The essential concept is to combine dynamic models of greenhouse temperature and CO2 concentration level with data-driven models that identify uncertainty in weather forecast error. By leveraging a machine learning approach, support vector clustering with weighted generalized intersection kernel, data-driven uncertainty sets for ambient temperature and solar radiation are constructed from historical weather data. A training-calibration procedure that tunes the size of uncertainty sets is implemented to ensure that data-driven uncertainty sets attain appropriate performance guarantee. In order to solve the optimization problem in DDRMPC, an affine disturbance feedback policy that provides tractable approximations of optimal control is utilized. A case study of controlling temperature and CO2 concentration level in a greenhouse is carried out. The results show that the proposed DDRMPC framework can prevent the greenhouse climate from becoming harmful to plant and fruit. DDRMPC approach ends up with 20 % less total economic cost than rule-based control strategy. The proposed DDRMPC approach also gives better control performance comparing to certainty equivalent MPC and robust MPC.

Global optimization in sustainable waste high-density polyethylene (HDPE) chemical recycling process is addressed under economic and environmental criteria. In this work, by far the most comprehensive superstructure of the HDPE recycling process with 867 processing routes is developed to produce valuable products from waste HDPE. Using the methodologies of life cycle assessment and techno-economic assessment, the superstructure optimization problem is then formulated as a multi-objective mixed-integer nonlinear fractional programming (MINFP) problem to address the sustainable waste HDPE recycling process with maximum unit net present value (NPV) and minimum levelized ReCiPe end-point score. A tailored parametric algorithm is utilized to efficiently convexify the fractional objective functions. With the help of the piecewise linearization method, nonlinear economic constraints can be linearized and effectively solved. This research proposes to use the ‘e-constraints’ method to obtain the Pareto-optimal curve. Results show that the optimal unit NPV ranges from -65 USD/t HDPE to 170 USD/t HDPE, and the levelized ReCiPe point of the most environmentally friendly design is 0.6 times of that of the most economically competitive design.

The conventional wastewater treatment facilities (WWTFs) are designed with the prime directive of simply purifying sewage by solids, pathogens, and nutrients removal. However, this ill-considered design philosophy stands chance to render the WWTFs unfavorably energy intensive and wasteful of nutrients. A promising route to raise the energy and nutrients utilization efficiencies of WWTFs is to retrofit the existing systems with smart upgrades for resource recovery. In this article, promising retrofits based on the benchmark design of WWTFs are proposed by virtue of process modifications and biosolids upcycling. This study directly assesses the economics and life cycle environmental impacts of the proposed retrofits of WWTFs, which are in turn compared to the reference design. Emerging thermochemical technologies and more complicated bioreactors are considered to maximize the biogas production, nutrient recovery, and biosolids upcycling. The results show the retrofit with hydrothermal liquefaction and secondary anaerobic digester demonstrates the highest net present value of - $ 97.93 MM, despite the highest annual production costs. The life cycle assessment results identify chlorination as the major contributor for most impact categories.

This work develops a novel dynamic material flow analysis (MFA)-based optimisation modelling framework for sustainable design of shale gas energy systems. This dynamic MFA-based framework provides high-fidelity modelling of complex material flow networks with recycling options, and it enables detailed accounting of time-dependent life cycle material flow profiles. Moreover, by incorporating a dimension of resource sustainability, the proposed modelling framework facilitates the sustainable supply chain design and operations with a more comprehensive perspective. The resulting optimisation problem is formulated as a mixed-integer linear fractional program and solved by an efficient parametric algorithm. To illustrate the applicability of the proposed modelling framework and solution algorithm, a case study of Marcellus shale gas supply chain is presented. The optimisation results help to identify clear trade-offs among economic, environmental, and resource performances in the shale gas energy system, and the MFA-based framework offers an effective way to find an optimal solution with well-balanced sustainability perfor

This paper aims to leverage the big data in shale gas industry for better decision making in optimal design and operations of shale gas supply chains under uncertainty. We propose a two-stage distributionally robust optimization model, where uncertainties associated with both the upstream shale well estimated ultimate recovery and downstream market demand are simultaneously considered. In this model, decisions are classified into first-stage design decisions as well as second-stage operational decisions. A data-driven approach is applied to construct the ambiguity set based on principal component analysis and first-order deviation functions. By taking advantage of affine decision rules, a tractable mixed-integer linear programming formulation can be obtained. The applicability of the proposed modeling framework is demonstrated through a case study of Marcellus shale gas supply chain. Comparisons with deterministic optimization models are investigated as well.

The petrochemical industry is an integral component of the global today. The petrochemical supply chain itself is a worldwide undertaking, and the final products that are offered to consumers will often travel thousands of miles from oil well to gas station pump. In the crude oil supply chain, there are three main stages: crude oil extraction, oil separation and refinement, and final product delivery. Within this supply chain, various companies will compete and attempt to maximize their profits by exploiting the demands and needs of the other companies within the supply chain. Each company or player in the crude oil industry has its own objective, and it will compete against other companies trying to pursue their individual objectives. In this work, the crude oil supply chain from oil well to refinery is modelled as a mixed-integer bilevel linear program (MIBLP) which accounts for conflicting objectives and interactions between different stakeholders. The composition, pricing, transportation distances, and environmental impacts of the different oils are taken into consideration when constructing the model. The resulting model is then applied to a U.S.-based refinery that purchases oil from various countries surrounding the Persian Gulf. The case study produces a pareto-optimal curve that displays the trade-off between environmental impact and profit. One intermediate point on the curve is selected to show how a 4.4 % decrease in profit can lead to a 3.0 % decrease in total kg CO2-eq produced.

Sustainability has become a key concern in process systems engineering. Renewable energy, such as biofuels, and renewable materials, such as bioproducts, could replace their non-renewable, petroleum-based counterparts. However, there remain many challenges in producing biofuels and bioproducts economically and efficiently. There are many different biomass feedstocks, processes to convert them, and many different possible biofuels and bioproducts to produce. Furthermore, prices and demands of biofuels and bioproducts are uncertain. The variation of price or demand of one bioproduct could influence price or demand of another, further complicating the problem. An approach that can identify economical, efficient, and sustainable biofuel and bioproduct production processes from the myriad possible options while also considering correlated and uncorrelated price and demand uncertainties of the final bioproducts is required. In this work, a data-driven decision-making framework is proposed for biomass processing network design that directly integrates machine learning with robust optimization. Principal component analysis (PCA) is used to identify latent uncertainties behind observed uncertainty data. A kernel density estimation approach captures probability distributions of the projected uncertainty data extracted from PCA. This uncertainty data analysis approach is applied to a bioconversion product and process network to identify cost-effective and environmentally-friendly biofuels and bioproducts production pathways. Our approach identifies a total annualized cost of $ 18.3M/y, 6 % lower than the cost found with conventional adaptive robust optimization.

In this paper, we address the sustainable design and operations of shale gas supply chains by proposing an integrated hybrid life cycle optimization (LCO) modelling framework. Unlike the traditional process-based LCO that suffers system truncation, the integrated hybrid LCO supplements the truncated system with a comprehensive economic input-output system. Meanwhile, the integrated hybrid LCO retains the precision in modelling major unit processes within the well-to-wire system boundary compared with the economic input-output-based LCO models. With the help of the integrated hybrid LCO framework, we can automatically identify the optimal sustainable alternatives in the design and operations of shale gas supply chains. To demonstrate the applicability, we present a case study of a well-to-wire shale gas supply chain located in the UK. According to the optimization results, the lowest levelized cost of electricity generated from shale gas is £51.8 /MWh, and the optimal life cycle GHG emissions are 473.5 kg CO2-eq/MWh.

This article addresses the optimal design of a geothermal binary trigeneration system for simultaneous cooling, heating, and power generation. A new processing network with alternative processing pathways is proposed. The major processing sections include geofluid production, Organic Rankine Cycle (ORC) evaporation, power generation, heat redistribution, cooling generation, condensing, and geofluid injection. Based on the superstructure, a cradle-to-gate life cycle analysis and techno-economic analysis are integrated with multiobjective optimization to simultaneously optimize the life cycle environmental impacts and the economic performance. To this end, a mixed integer nonlinear programming (MINLP) model is proposed to select the optimal processing pathway in terms of both greenhouse gas emissions and total annualized cost. A case study is also presented to cope with the seasonal demand of cooling, heating, and power to illustrate the application of the proposed modelling and solution methods.

In this work, we propose a superstructure of integrated shale gas processing and chemical manufacturing processes with 51,840 alternative possible process designs. The superstructure consists of eight sections, namely acid gas removal, dehydration, NGLs recovery, NGLs separation, hydrocarbons conversion, light olefins separation, C4 separation, and acid gas disposal. For the steam cracking reactions in the hydrocarbons conversion section, we optimize the product distributions of steam cracking of ethane, propane, n-butane, and i-butane. Extensive process simulations are performed for all the involved processes in the superstructure in order to collect high-fidelity process data and develop detailed process models for the technology/process alternatives in the superstructure. Next, we propose a multiobjective mixed-integer nonlinear programming (MINLP) superstructure optimization model with five groups of constraints, namely superstructure network configuration constraints, mass balance constraints, energy balance constraints, techno-economic evaluation constraints, environmental impact assessment constraints. Three objective functions are maximizing the net present value per GJ of raw shale gas, minimizing the global warming potential per GJ of raw shale gas, and minimizing the water footprint per GJ of raw shale gas, respectively. A tailored global optimization algorithm is applied to efficiently solve the resulting nonconvex MINLP problem. The application of the proposed framework is illustrated through a case study based on a Marcellus shale gas feed.

Process scheduling is one key layer of decision hierarchy for process industries to optimize their production schedule in order to gain the long-term economic viability. Besides coordinating limited available resources and satisfying demands on production quantity, quality, and environmental restrictions, the challenge of process scheduling also lies in the treat of uncertainties when approaching the multistage adjustable robust optimization (ARO) of the scheduling problem. In this work, we solve the robust optimization problem for batch process scheduling under uncertainty by using piecewise linear decision rule (PLDR). Based on PLDR and the Dirichlet process Gaussian mixture model (DPGMM) which is for data-driven uncertainty set construction, we demonstrate with an industrial process case study that the combination of the data-driven uncertainty set and the piecewise linear decision rule is capable of generating usually better, at least as good, batch process scheduling optimization solutions in comparison to some conventional ARO approaches.

Resilience in response to disruption events is critical to the economic performance of process systems, but this concept has received limited attention in the literature. A general framework for resilience optimization is proposed to incorporate an improved quantitative measure of resilience and a comprehensive set of resilience enhancement strategies for process design and operations. The proposed framework identifies a set of disruptive events for a given system, and then formulates a multiobjective two-stage adaptive robust mixed-integer fractional programming model to optimize the resilience and economic objectives simultaneously. The model accounts for network configuration, equipment capacities, and capital costs in the first stage, and the number of available processes and operating levels in each time period in the second stage. The applicability of the proposed framework is demonstrated through an application on process network design and planning.

Supply chains are normally managed in a decentralized way by multiple stakeholders pursuing distinct objectives. However, most existing supply chain studies rely on centralized models and neglect the uncertain behaviors of stakeholders in the decision-making process. In this work, a novel game theory based stochastic model is proposed that integrates two-stage stochastic programming with a single-leader-multiple-follower Stackelberg game scheme. The aim is to address the optimization problem of decentralized supply chains considering multiple stakeholders under uncertainty. The resulting model is formulated as a 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. To illustrate the applicability of proposed modeling framework, a case study of a large-scale shale gas supply chain is presented, which demonstrates the advantages of the proposed modeling framework and efficiency of the solution algorithm.