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Fuel cells are widely deemed as a sustainable cogeneration technology that promises great decarbonization potential. However, economic operations of such sustainable energy systems are challenging ...

Chemical recycling has the potential to reduce the environmental impacts from waste plastics, mitigate climate change, and contribute to circular economy. This study compares the environmental and ...

A promising route to transition wastewater treatment facilities (WWTFs) from energy-consuming to net energy-positive is to retrofit existing facilities with process modifications, residual biosolid upcycling, and effluent thermal energy recovery. This study assesses the economics and life cycle environmental impacts of three proposed retrofits of WWTFs that consider thermochemical conversion technologies, namely, hydrothermal liquefaction, slow pyrolysis, and fast pyrolysis, along with advanced bioreactors. The results are in turn compared to the reference design, showing the retrofitting design with hydrothermal liquefaction, and an up-flow anaerobic sludge blanket has the highest net present value (NPV) of $177.36MM over a 20-year plant lifetime despite 15% higher annual production costs than the reference design. According to the ReCiPe method, chlorination is identified as the major contributor for most impact categories in all cases. There are several uncertainties embedded in the techno-economic analysis and life cycle assessment, including the discount rate, capital investment, sewer rate, and prices of main products; among which, the price of biochar presents the widest variation from $50 to $1900/t. Sensitivity analyses reveal that the variation of discount rates causes the most significant changes in NPVs. The impact of the biochar price is more pronounced in the slow pyrolysis-based pathway compared to the fast pyrolysis since biochar is the main product of slow pyrolysis.

With the rapid rise in global population over the past decades, there has been a corresponding surge in demand for resources such as food and energy. As a consequence, the rate of waste generation and resultant pollution levels have risen drastically. Currently, most organic solid wastes are either land applied or sent to landfills, with the remaining fraction incinerated or anaerobically digested. However, with the current emphasis on the reduction of emissions, nutrient recovery, clean energy production and circular economy, it is important to revisit some of the conventional methods of treating these wastes and tap into their largely unrealized potential in terms of environmental and economic benefits. Wastewater sludge, with its high organic content and fairly constant supply, provides a great opportunity to implement some of these strategies using thermochemical conversion technologies, which are considered as one of the alternatives for upcycling such waste streams. This paper summarizes the results of prominent studies for valorizing wastewater sludge through thermochemical conversion technologies while drawing inferences and identifying relationships between different technical and operating parameters involved. This is followed by sections emphasizing the environmental and economic implications of these technologies, and their corresponding products in context of the broader fields of waste-to-energy, nutrient recycling and the progress towards a circular economy.

Thermochemical technologies provide promising pathways to recover energy and reduce environmental impacts from biomass wastes. Poultry manure or litter additionally provides an opportunity for reco...

This study examines prominent thermochemical conversion technologies, such as slow pyrolysis, fast pyrolysis, gasification and hydrothermal liquefaction, for treating poultry litter in New York Sta...

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.

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

The application of deep learning to a diverse array of research problems has accelerated progress across many fields, bringing conventional paradigms to a new intelligent era. Just as the roles of instrumentation in the old chemical revolutions, we reinforce the necessity for integrating deep learning in molecular systems engineering and design as a transformative catalyst towards the next chemical revolution. To meet such research needs, we summarize advances and progress across several key elements of molecular systems: molecular representation, property estimation, representation learning, and synthesis planning. We further spotlight recent advances and promising directions for several deep learning architectures, methods, and optimization platforms. Our perspective is of interest to both computational and experimental researchers as it aims to chart a path forward for cross-disciplinary collaborations on synthesizing knowledge from available chemical data and guiding experimental efforts.

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.

This paper investigates the problem of designing data-driven stochastic Model Predictive Control (MPC) for linear time-invariant systems under additive stochastic disturbance, whose probability distribution is unknown but can be partially inferred from data. We propose a novel online learning based risk-averse stochastic MPC framework in which Conditional Value-at-Risk (CVaR) constraints on system states are required to hold for a family of distributions called an ambiguity set. The ambiguity set is constructed from disturbance data by leveraging a Dirichlet process mixture model that is self-adaptive to the underlying data structure and complexity. Specifically, the structural property of multimodality is exploited, so that the first- and second-order moment information of each mixture component is incorporated into the ambiguity set. A novel constraint tightening strategy is then developed based on an equivalent reformulation of distributionally robust CVaR constraints over the proposed ambiguity set. As more data are gathered during the runtime of the controller, the ambiguity set is updated online using real-time disturbance data, which enables the risk-averse stochastic MPC to cope with time-varying disturbance distributions. The online variational inference algorithm employed does not require all collected data be learned from scratch, and therefore the proposed MPC is endowed with the guaranteed computational complexity of online learning. The guarantees on recursive feasibility and closed-loop stability of the proposed MPC are established via a safe update scheme. Numerical examples are used to illustrate the effectiveness and advantages of the proposed MPC.

Quantum computing and deep learning methods hold great promise to open up a new era of computing and have been receiving significant attention recently. This paper presents quantum computing (QC) based deep learning methods for fault diagnosis that are capable of overcoming the computational challenges faced by conventional techniques performed on classical computers. The shortcomings of such classical data-driven techniques are addressed by the proposed QC-based fault diagnosis model. A quantum computing assisted generative training process followed by supervised discriminative training is used to train this model. The applicability of proposed model and methods is demonstrated by applying them to process monitoring of Tennessee Eastman (TE) process. The proposed QC-based deep learning approach enjoys superior performance with an average fault diagnosis rate of 80% and tremendously low false alarm rates for the TE process.

Quantum computing (QC) and deep learning techniques have attracted widespread attention in the recent years. This paper proposes QC-based deep learning methods for fault diagnosis that exploit their unique capabilities to overcome the computational challenges faced by conventional data-driven approaches performed on classical computers. Deep belief networks are integrated into the proposed fault diagnosis model and are used to extract features at different levels for normal and faulty process operations. The QC-based fault diagnosis model uses a quantum computing assisted generative training process followed by discriminative training to address the shortcomings of classical algorithms. To demonstrate its applicability and efficiency, the proposed fault diagnosis method is applied to process monitoring of continuous stirred tank reactor (CSTR) and Tennessee Eastman (TE) process. The proposed QC-based deep learning approach enjoys superior fault detection and diagnosis performance with obtained average fault detection rates of 79.2% and 99.39% for CSTR and TE process, respectively.

The purpose of this paper is to explore the applications of quantum computing to energy systems optimization problems and discuss some of the challenges faced by quantum computers with techniques to overcome them. The basic concepts underlying quantum computation and their distinctive characteristics in comparison to their classical counterparts are also discussed. Along with different hardware architecture description of two commercially available quantum systems, an example making use of open-source software tools is provided as a first step for diving into the new realm of programming quantum computers for solving systems optimization problems. The trade-off between qualities of these two quantum architectures is also discussed. Complex nature of energy systems due to their structure and large number of design and operational constraints make energy systems optimization a hard problem for most available algorithms. Problems like facility location allocation for energy systems infrastructure development, unit commitment of electric power systems operations, and heat exchanger network synthesis which fall under the category of energy systems optimization are solved using both classical algorithms implemented on conventional CPU based computer and quantum algorithm realized on quantum computing hardware. Their designs, implementation and results are stated. Additionally, this paper describes the limitations of state-of-the-art quantum computers and their great potential to impact the field of energy systems optimization.

This paper presents a novel deep learning based data-driven optimization method. A novel generative adversarial network (GAN) based data-driven distributionally robust chance constrained programming framework is proposed. GAN is applied to fully extract distributional information from historical data in a nonparametric and unsupervised way without a priori approximation or assumption. Since GAN utilizes deep neural networks, complicated data distributions and modes can be learned, and it can model uncertainty efficiently and accurately. Distributionally robust chance constrained programming takes into consideration ambiguous probability distributions of uncertain parameters. To tackle the computational challenges, sample average approximation method is adopted, and the required data samples are generated by GAN in an end-to-end way through the differentiable networks. The proposed framework is then applied to supply chain optimization under demand uncertainty. The applicability of the proposed approach is illustrated through a county-level case study of a spatially explicit biofuel supply chain in Illinois.

This work proposes an optimization approach for designing efficient water networks for the shale gas production through the recycle and reuse of wastewater streams reducing the freshwater consumption and effluents considering economic and environmental goals. The economic objective function aims to minimize the total annual cost for the water network including the costs associated with storage, treatment and disposal (capital cost) as well as freshwater cost, treatment cost and transportation costs. The environmental objective is addressed to deal with the minimization of the environmental impact associated with the discharged concentration of total dissolved solids in the wastewater streams and the freshwater consumption through an environmental function that represents the benefit for removing pollutants using the eco-indicator 99 methodology. The methodology requires a given scheduling for the completion phases of the target wells to be properly implemented by the available hydraulic fracturing crews during a time horizon. The model formulation is configured to determine the optimal sizes for the equipment involved by the project, particularly the sizes for storage and treatment units are quantified by the optimization process. A case study is solved to evaluate the effectiveness of the proposed optimization approach.

This paper is concerned with the resilient design and operations of process systems in response to disruption events. A general framework for resilience optimization is proposed that incorporates 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. A tailored solution algorithm is developed to tackle the computational challenge of the resulting multi-level optimization problem. The applicability of the proposed framework is illustrated through applications on a chemical process network and a shale gas processing system.

We propose a novel modeling framework integrating the dynamic material flow analysis (MFA) approach with life cycle optimization (LCO) methodology for sustainable design of energy systems. This dynamic MFA-based LCO framework provides high-fidelity modeling of complex material flow networks with recycling options, and it enables detailed accounting of time-dependent life cycle material flow profiles. The decisions regarding input, output, and stock of materials are seamlessly linked to their environmental impacts for rigorous quantification of environmental consequences. Moreover, by incorporating an additional dimension of resource sustainability, the proposed modeling framework facilitates the sustainable supply chain design and operations with a more comprehensive perspective. The resulting optimization problem is formulated as a mixed-integer linear fractional program and solved by an efficient parametric algorithm. To illustrate the applicability of the proposed modeling framework and solution algori...

Life cycle environmental impacts of a product or process may be global and/or spatially-explicit, such as land use change (LUC) and LUC greenhouse gas (GHG) emissions. Life cycle optimization (LCO) usually does not account for these impacts. However, for a product or process to be truly sustainable, they must be considered. We integrate computable general equilibrium (CGE)-based LUC modeling and LCO to create a novel multiobjective CGE-LUC-LCO framework to account for global environmental impacts and production costs. The framework is then applied to case studies on life cycle GHG emissions throughout the bioethanol life cycle, considering regional and global agricultural practices, land use, technological impacts, and global economic forces. The framework considers emissions from feedstock production, transportation, direct/indirect processing emissions, end use, and LUC. The model allows for selection of 16 bioethanol production pathways from 5 feedstocks, and 3 case studies with US and EU bioethanol demands are examined. The methodology identifies cleaner production strategies by considering global, spatially-explicit life cycle environmental impacts like LUC and LUC GHG emissions for the first time in LCO.

This paper proposes a novel data-driven robust optimization framework that leverages the power of machine learning and big data analytics for decision-making under uncertainty. By applying principal component analysis to uncertainty data, correlations between uncertain parameters are effectively captured, and latent uncertainty sources are identified. These data are then projected onto each principal component to facilitate extracting distributional information of latent uncertainties using kernel density estimation techniques. To explicitly account for asymmetric distributions, we introduce forward and backward deviation vectors into the data-driven uncertainty set, which are further incorporated into novel data-driven static and adaptive robust optimization models. The proposed framework not only significantly ameliorates the conservatism of robust optimization, but also enjoys computational efficiency and wide-ranging applicability. Three applications on optimization under uncertainty, including model predictive control, batch production scheduling, and process network planning, are presented to demonstrate the applicability of the proposed framework.

This paper presents multi-criteria environmental and economic analyses of pulverized coal power plants with various advanced CO2 capture and separation (CCS) technologies, including oxy-fuel combustion (Oxy), calcium looping post-combustion capture (CaL), combination of Oxy with CaL (Oxy-CaL) and Oxy with chemical looping air separation (Oxy-CLAS). The life cycle analysis (LCA) and techno-economic analysis (TEA) are integrated with Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approaches. This methodology is applied to forecast the potential of incorporating CaL or CLAS into oxy-fuel combustion and identify the most promising CCS technology option for pulverized coal power plants from the perspectives of different stakeholders. The results show that application of CCS reduces the ecosystem quality and the human health impacts, but increases the resources use and yields an economic penalty of $12.76∼$33.33 per ton of CO2 avoidance. From the perspective of industry only, CCS has an unfavorable effect on the performance of the pulverized coal power plant, and the promotion in carbon price is critical for CCS to attract the support from industry. In terms of the four CCS technologies, Oxy-CLAS comprehensively performs the best, followed by Oxy. Decrement of consumption of Ca-based sorbents is critical for Oxy-CaL to outrank Oxy.

The development of computationally efficient, accurate, and stable dynamic reduced order models of Shell Entrained-flow gasifiers would help to better understand the influence of design variables, feedstocks, and processing conditions on the operating performance of the reactors. This work presents a novel dynamic model of a commercial-scale Shell entrained-flow gasifier for on-line process simulation and soft-measurement of relevant process variables. A dynamic mathematical model of the reactor is developed to obtain real-time performance data of some unmeasurable variables and assess dynamic performance of the reactor under different operating conditions. The model consists of several sub-models for devolatilization and combustion, gasification, slagging, and heat transfer. To validate the model, the simulation results are compared with the literature data. Sensitivity analysis is further performed for process optimization. Furthermore, dynamic characteristics is analyzed and optimal operational strategies for industrial Shell entrained-flow gasifier is obtained by optimizing the ratio of oxygen with carbon and the ratio of coal with carrier CO2.

Gasification processes with complex reaction systems typically require stable operating condition. However, variations of feedstock flow, composition of feedstock, and environmental factors, as well as other factors, may cause abnormal operating conditions. This work proposes a novel systems modeling and analysis method by combining computational fluid dynamics (CFD) and process simulation for the Shell pulverized coal gasifier. The proposed method considers the Shell pulverized coal entrained-flow gasifier with two parts: a gasification core zone and a heat exchange and water gas shift zone. High-fidelity CFD models of gasification core zone is developed to obtain characteristics of flow field, temperature field and composition profiles within the gasification core zone. An equation-oriented process simulation model is further developed for the heat exchange & water gas shift zone. The proposed hybrid method is validated by comparing with industrial operating data. Three cases for abnormal operating condition are further investigated with the proposed hybrid model. The most significant factors that influence the process operability are found to be the characteristics of gas and particles hydrodynamic behaviors of the inner layer of the gasification core zone. The results show that obvious vortex for the gas and particles is beneficial to the normal and abnormal operating conditions. To improve the operability of the entrained-flow gasifier under abnormal operating condition, it is crucial to keep the swirl zone of the vortex at the center of the reactor. In the end, an improved design for gasifier is presented by adjusting the bias angle of the nozzle to make the swirl zone of the vortex more obvious. According to the simulation results, the optimal bias angle is 5.0*(π/180) rad for the gasifier under both nominal and abnormal conditions.

Nylon-6 and nylon-6,6 processes produce considerable amount of organic waste (known as light oil) consisting of n-pentanol, cyclohexanone, and cyclohexene oxide, which are difficult to separate and recover. This Article proposes six novel process designs to separate the light oil into three value-added products based on azeotropic distillation using water as an entrainer. These azeotropic distillation process designs take into account direct sequence, indirect sequence, thermal coupled column, and three types of dividing wall columns (dividing wall at the top, bottom, and middle of columns, respectively) for entrainer recovery. A conventional distillation process design for separation of the same light oil is also modeled and analyzed for comparison. High-fidelity process simulations are performed for each of the seven process designs in Aspen Plus. We further conduct exergy analyses and technoeconomic analyses to evaluate and compare the exergy efficiencies and economic performances of these seven proces...

A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for optimization under uncertainty leveraging labeled multi-class uncertainty data. Uncertainty data in large datasets are often collected from various conditions, which are encoded by class labels. Machine learning methods including Dirichlet process mixture model and maximum likelihood estimation are employed for uncertainty modeling. A DDSRO framework is further proposed based on the data-driven uncertainty model through a bi-level optimization structure. The outer optimization problem follows a two-stage stochastic programming approach to optimize the expected objective across different data classes; adaptive robust optimization is nested as the inner problem to ensure the robustness of the solution while maintaining computational tractability. A decomposition-based algorithm is further developed to solve the resulting multi-level optimization problem efficiently. Case studies on process network design and planning are presented to demonstrate the applicability of the proposed framework and algorithm.

Distributionally robust optimization (DRO) is an emerging and effective method to address the inexactness of probability distributions of uncertain parameters in decision-making under uncertainty. We propose an effective DRO framework for planning and scheduling under demand uncertainties. A novel data-driven approach is proposed to construct ambiguity sets based on principal component analysis and first-order deviation functions, which help excavating accurate and useful information from uncertainty data. Moreover, it leads to mixed-integer linear reformulations of planning and scheduling problems. To account for the multi-stage sequential decision-making structure in process operations, we further develop multi-stage DRO models and adopt affine decision rules to address the computational issue. Applications in industrial-scale process network planning and batch process scheduling demonstrate that, the proposed DRO approach can effectively leverage uncertainty data information, better hedge against distributional ambiguity, and yield more profits.

This paper presents comparative technoeconomic and environmental analyses of three ethylene manufacturing pathways based on ethane-rich shale gas, corn stover, and corn grain. The shale-gas-based pathway includes two processing steps, namely, shale gas processing to produce ethane and ethane steam cracking to manufacture ethylene. The two biomass-based pathways also contain two processing steps each, namely, bioethanol production via fermentation and ethylene manufacturing via bioethanol dehydration. A distributed–centralized processing network that consists of distributed ethane/bioethanol production and centralized ethylene manufacturing is employed for each of the three pathways. Detailed process simulation models are developed for major processing steps, and the three pathways are then modeled on five different ethylene production scales. On the basis of the detailed mass and energy balances and life cycle inventory results, we conduct technoeconomic and life cycle analyses to systematically compare t...

A novel framework based on stochastic modeling methods and likelihood analysis to address large-scale monetization processes of converting shale gas to polymers under the mixed uncertainties of feedstock compositions, estimated ultimate recovery, and economic parameters is presented. A new stochastic data processing strategy is developed to quantify the feedstock variability through generating the appropriate number of scenarios. This strategy includes the Kriging-based surrogate model, sample average approximation, and the integrated decline-stimulate analysis curve. The feedstock variability is then propagated through performing a detailed techno-economic modeling method on distributed-centralized conversion network systems. Uncertain economic parameters are incorporated into the stochastic model to estimate the maximum likelihood of performance objectives. The proposed strategy and models are illustrated in four case studies with different plant locations and pathway designs. The results highlight the benefits of the hybrid pathway as it is more amenable to reducing the economic risk of the projects. © 2017 American Institute of Chemical Engineers AIChE J, 2017

Regret is defined as the deviation of objective value from the perfect information solution, and serves as an important evaluation metric for decision-making under uncertainty. This paper proposes a novel framework that effectively incorporates the minimax regret criterion into two-stage adaptive robust optimization (ARO). In addition to the conventional robustness criterion, this ARO framework also simultaneously optimizes the worst-case regret to push the performance of the resulting solution towards the utopia one under perfect information. By using a data-driven uncertainty set, we formulate a multiobjective ARO problem that generates a set of Pareto-optimal solutions to reveal the systematic trade-offs between the conventional robustness and minimax regret criteria. The resulting multi-level mixed-integer programming problem cannot be solved directly by any off-the-shelf optimization solvers, so we further propose tailored column-and-constraint generation algorithms to address the computational challenge. Two applications on process network planning and batch process scheduling are presented to demonstrate the applicability of the proposed framework and the efficiency of the proposed solution algorithms.

Data-driven robust optimization has attracted immense attentions. In this work, we propose a data-driven uncertainty set for robust optimization under high-dimensional uncertainty. We propose to first decompose the high-dimensional data space into the principal subspace and the residual subspace by employing principal component analysis, and then adopt support vector clustering and classic polyhedral uncertainty set to describe the intricate geometry in the principal subspace and the tiny variations in the residual subspace, respectively, giving rise to a new data-driven uncertainty set. Similar to classic uncertainty sets, the proposed data-driven uncertainty set can also preserve the tractability of robust optimization problems. In addition, we establish the probabilistic guarantee theoretically by further calibrating the uncertainty set with an independent dataset, which ensures that the data-driven uncertainty set covers a portion of uncertainty with a given confidence level. Numerical results show the effectiveness of the proposed uncertainty set in reducing conservatism of robust optimization problems as well as the fidelity of the established probabilistic guarantee.

Process network planning is an important and challenging task in process systems engineering. Due to the penetration of uncertainties such as random demands and market prices, stochastic programming and robust optimization have been extensively used in process network planning for better protection against uncertainties. However, both methods fall short of addressing the ambiguity of probability distributions, which is quite common in practice. In this work, we apply distributionally robust optimization to handling the inexactness of probability distributions of uncertain demands in process network planning problems. By extracting useful information from historical data, ambiguity sets can be readily constructed, which seamlessly integrate statistical information into the optimization model. To account for the sequential decision-making structure in process network planning, we further develop multi-stage distributionally robust optimization models and adopt affine decision rules to address the computational issue. Finally, the optimization problem can be recast as a mixed-integer linear program. Applications in industrial-scale process network planning demonstrate that, the proposed distributionally robust optimization approach can better hedge against distributional ambiguity and yield rational long-term decisions by effectively utilizing demand data information.

This paper analyzes the life cycle environmental impacts of shale gas by using an integrated hybrid life cycle analysis (LCA) and optimization approach. Unlike the process-based LCA that suffers system truncation, the integrated hybrid LCA supplements the truncated system with a comprehensive economic input-output system. Compared with the economic input–output-based LCA that loses accuracy from process aggregation, the integrated hybrid LCA retains the precision in modeling major unit processes within the well-to-wire system boundary. Three environmental categories, namely, life cycle greenhouse gas emissions, water consumption, and energy consumption, are considered. Based on this integrated hybrid LCA framework, we further developed an integrated hybrid life cycle optimization model, which enables automatic identification of sustainable alternatives in the design and operations of shale gas supply chains. We applied the model to a well-to-wire shale gas supply chain in the UK to illustrate the applicab...

In this work, we address the life cycle economic and environmental optimization of a supply chain network considering both design and operational decisions under uncertainty. A modeling framework is proposed that integrates the functional-unit-based life cycle optimization methodology and the two-stage stochastic programming approach for sustainable supply chain optimization under uncertainty. We develop a stochastic mixed-integer linear fractional programming (SMILFP) model to tackle multiple uncertainties regarding feedstock supply and product demand. To address the computational challenge of solving the resulting large-scale SMILFP problems, an efficient solution algorithm is developed that takes advantage of the efficiency of parametric algorithm and the decomposition-based multi-cut L-shaped method. We present a case study based on a spatially explicit model for the optimal design and operations of a county-level hydrocarbon biofuel supply chain in Illinois to demonstrate the applicability of the proposed modeling framework and the efficiency of the solution algorithm.

The thickener is used to provide slurries with a stable and satisfactory concentration in the ore dressing plant. To efficiently control an industrial thickener, a soft sensor model should be built first to predict the underflow concentration. In industrial sites, it is usually expensive and time-consuming to collect sufficient high-quality data to develop a data-driven model. In this work, a nonlinear regression method for transfer learning is proposed to solve this problem, which is named domain transfer functional-link neural network (DT-FLNN). The framework of the proposed method includes two stages, and the issue of domain adaption is separately considered at each stage. In the first stage, the activation matrix of the source domain is reconstructed to narrow the distribution difference, and the augmented input matrices of the source and target domains are formulated. Then, the latent variable (LV) based linear regression method for transfer learning is performed at the second stage to train the FLNN of the target domain, and the task of domain adaption is realized by introducing a regularization term. Besides, a systematic method is also presented to determine the hyper-parameters in the proposed DT-FLNN method. The efficiency of the proposed method is evaluated by employing a numerical example and an industrial application. Compared with other nonlinear regression approaches for transfer learning, the proposed method can further increase the prediction accuracy and reduce the influence of noise.

This paper presents multicriteria environmental and economic analyses of municipal solid waste (MSW) grate incineration power plants without and with CO2 capture and separation (CCS) technologies, including monoethanolamine (MEA) absorption, pressure/vacuum swing adsorption (P/VSA), and oxy-fuel combustion (Oxy). The life-cycle analysis (LCA) and techno-economic analysis (TEA) are integrated with the analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) approaches for systematic environmental and economic analysis. This systematic methodology is applied to investigate the applicability of CCS technologies in MSW incineration power plants from perspectives of local government, enterprise, residents, and “equal” weight. The results show that application of CCS reduces the ecosystem quality and the human health impacts, but increases the resource use and yields an economic penalty of from ∼$33.45 to ∼$45.98 per ton of CO2 avoidance. From the perspective ...