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This article addresses the sustainable design of hydrogen (H2) production systems that integrate brown and blue pathways with green hydrogen infrastructure. We develop a systematic framework to simultaneously optimize the process superstructure and operating conditions of steam methane reforming (SMR)‐based hydrogen production systems. A comprehensive superstructure that integrates SMR with multiple carbon dioxide capture technologies, electrolyzers, fuel cells, and working fluids in the organic rankine cycle is proposed under varying operating conditions. A life cycle optimization model is then developed by integrating superstructure optimization, life cycle assessment approach, techno‐economic assessment, and process optimization using extensive process simulation models and formulated as a mixed‐integer nonlinear program. We find that the optimal unit‐levelized cost of hydrogen ranges from $1.49 to $3.18 per kg H2. Moreover, the most environmentally friendly process attains net‐zero life cycle greenhouse gas emissions compared to 10.55 kg CO2‐eq per kg H2 for the most economically competitive process design. [ABSTRACT FROM AUTHOR]

In this work, we present a systematic approach for solvent selection and process intensification synthesis based on the Generalized Modular Representation Framework (GMF). GMF is a bottom‐up process synthesis strategy which leverages mass and/or heat transfer building blocks to represent chemical equipment and flowsheets. Thus, conventional and/or intensified process solutions can be generated to obtain optimal process design(s) without a pre‐postulation of equipment or flowsheet configurations. To address the challenge of module dimensionality estimation for capital cost correlation, orthogonal collocation is coupled to enhance GMF intra‐module representation. Material selection is achieved by incorporating physical property models (e.g., NRTL) in GMF model formulation to describe the nonideal liquid mixture behaviors and to assess solvent performance in facilitating separation. In this context, solvent selection and process intensification synthesis are simultaneously addressed within a superstructure‐based optimization formulation. The proposed approach is demonstrated on two case studies for ethanol‐water extractive separation, with two sets of solvents evaluated: (a) ethylene glycol versus methanol to obtain 99 mol% ethanol, and (b) ethylene glycol versus 1‐ethyl‐3‐methyl‐imidazolium acetate ([EMIM][OAc]) to obtain 99.8 mol% ethanol and 99 mol% water. [ABSTRACT FROM AUTHOR]

This article addresses the sustainable design and synthesis of open‐loop recycling process of waste high‐density polyethylene (HDPE) under both environmental and economic criteria. We develop by far the most comprehensive superstructure for producing monomers, aromatic mixtures, and fuels from waste HDPE. The superstructure optimization problem is then formulated as a multi‐objective mixed‐integer nonlinear fractional programming (MINFP) problem to simultaneously optimize the unit net present value (NPV) and unit life cycle environmental impacts. A tailored global optimization algorithm integrating the inexact parametric algorithm with the branch‐and‐refine algorithm is applied to efficiently solve the resulting nonconvex MINFP problem. Results show that the optimal unit NPV ranges from $107.2 to $151.3 per ton of HDPE treated. Moreover, the unit life cycle greenhouse gas emissions of the most environmentally friendly HDPE recycling process are 0.40 ton CO2‐eq per ton of HDPE treated, which is 63% of that of the most economically competitive process design. [ABSTRACT FROM AUTHOR]

We propose a general shortcut distillation column model that can address a wide range of types of separations including nonsharp/sloppy splits. The model can automatically identify key components and estimate the energy requirement of a desired separation in the presence, potentially, of components with zero flow rates. To obtain more practical column height and vapor flow rates, we augment the model with constraints inspired by the Fenske equation. We also propose valid constraints to enhance the computational performance of the proposed model. Due to its flexibility and computational efficiency, the proposed model can be readily used for superstructure‐based process synthesis. [ABSTRACT FROM AUTHOR]

The key to many chemical and energy conversion processes is the choice of the right molecule, for example, used as working fluid. However, the choice of the molecule is inherently coupled to the choice of the right process flowsheet. In this work, we integrate superstructure‐based flowsheet design into the design of processes and molecules. The thermodynamic properties of the molecule are modeled by the PC‐SAFT equation of state. Computer‐aided molecular design enables considering the molecular structure as degree of freedom in the process optimization. To consider the process flowsheet as additional degree of freedom, a superstructure of the process is used. The method results in the optimal molecule, process, and flowsheet. We demonstrate the method for the design of an organic Rankine cycle considering flowsheet options for regeneration, reheating, and turbine bleeding. The presented method provides a user‐friendly tool to solve the integrated design problem of processes, molecules, and process flowsheets. [ABSTRACT FROM AUTHOR]

This article addresses the sustainable design of organic Rankine cycle-based geothermal binary power systems under economic and environmental criteria. A novel super-structure with multiple heat source temperatures, working fluids, and heat rejection systems is proposed. Based on the superstructure, a life cycle optimization model is formulated as a mixed-integer nonlinear fractional program (MINFP) to determine the optimal design. The nonconvex MINFP is efficiently solved by a tailored global optimization algorithm. Two case studies are considered to demonstrate the proposed modeling framework and solution algorithm. One case is based on a geothermal energy system located in California, and the other one is in New York (NY) State. The results show that the geothermal energy system in California is much more economically competitive than that in NY State. The difference in life cycle environmental impacts is less pronounced because the environmental impacts are less sensitive to geological conditions than the capital investments. [ABSTRACT FROM AUTHOR]

Sustainable polymers derived from biomass have great potential to replace petrochemical based polymers and fulfill the ever-increasing market demand. To facilitate their industrialization, in this research, a comprehensive superstructure reaction network comprising a large number of reaction pathways from biomass to both commercialized and newly proposed polymers is constructed. To consider economic performance and environmental impact simultaneously, both process profit and green chemistry metrics are embedded into the multiobjective optimization framework, and MINLP is used to enable the effective selection of promising biopolymer candidates. Through this proposed approach, this study identifies the best biopolymer candidates and their most profitable and environmentally friendly synthesis routes under different scenarios. Moreover, the stability of optimization results regarding the price of raw materials and polymers and the effect of process scale on the investment cost are discussed in detail. These results, therefore, pave the way for future research on the production of sustainable biopolymers. © 2017 American Institute of Chemical Engineers AIChE J, 63: 91-103, 2018 [ABSTRACT FROM AUTHOR]

We propose a novel process synthesis framework that combines product distribution optimization of chemical reactions and superstructure optimization of the process flowsheet. A superstructure with a set of technology/process alternatives is first developed. Next, the product distributions of the involved chemical reactions are optimized to maximize the profits of the effluent products. Extensive process simulations are then performed to collect high-fidelity process data tailored to the optimal product distributions. Based on the simulation results, a superstructure optimization model is formulated as a mixed-integer nonlinear program (MINLP) to determine the optimal process design. A tailored global optimization algorithm is used to efficiently solve the large-scale nonconvex MINLP problem. The resulting optimal process design is further validated by a whole-process simulation. The proposed framework is applied to a comprehensive superstructure of an integrated shale gas processing and chemical manufacturing process, which involves steam cracking of ethane, propane, n-butane, and i-butane. © 2017 American Institute of Chemical Engineers AIChE J, 63: 123-143, 2018 [ABSTRACT FROM AUTHOR]

Hydrogen network design is an important step in hydrogen management of a petroleum refinery that manages the hydrogen distribution and consumption in a cost-effective manner. While most works in this area have primarily focused on minimization of fresh hydrogen requirement and hydrogen purification aspects, very few works have dealt the issue of compression costs in hydrogen network designs. This work proposes a new mathematical model for synthesizing a hydrogen network with minimum compression costs. In contrast to the existing literature, this model uses stream-dependent properties and realistic compressor cost correlations to determine the compression duty and costs, respectively. Tests on literature examples show that our model is flexible and gives reasonably favorable solutions than the previous models. Furthermore, the usefulness of understanding the trade-offs between the number of compressors and compression duty and the importance of using stream-dependent conditions in estimating compression costs are also highlighted in this work. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3925-3943, 2017 [ABSTRACT FROM AUTHOR]

Fractional metrics, such as return on investment (ROI), are widely used for performance evaluation, but uncertainty in the real market may unfortunately diminish the results that are based on nominal parameters. This article addresses the optimal design of a large-scale processing network for producing a variety of algae-based fuels and value-added bioproducts under uncertainty. We develop by far the most comprehensive processing network with 46,704 alternative processing pathways. Based on the superstructure, a two-stage adaptive robust mixed integer fractional programming model is proposed to tackle the uncertainty and select the robust optimal processing pathway with the highest ROI. Since the proposed problem cannot be solved directly by any off-the-shelf solver, we develop an efficient tailored solution method that integrates a parametric algorithm with a column-and-constraint generation algorithm. The resulting robust optimal processing pathway selects biodiesel and poly-3-hydroxybutyrate as the final fuel and bioproduct, respectively. © 2016 American Institute of Chemical Engineers AIChE J, 63: 582-600, 2017 [ABSTRACT FROM AUTHOR]

In this paper, eight optimal biorefinery concepts for biofuels and biochemicals production are critically analyzed and compared in terms of their techno-economic performance and associated economic risks against historical market fluctuations. The investigated biorefinery concepts consider different combinations of biomass feedstock (lignocellulosic versus algal) and conversion technologies (biochemical versus thermochemical). In addition, the economic performance of each biorefinery concept is tested assuming a sudden drop in oil prices in order to compare the fitness/survival of each concept under extreme market disturbances. The analyses reveal amongst others that: (i) lignocellulosic bioethanol production is not economically feasible considering a drop in oil prices (a negative internal rate of return); (ii) a multi-product biorefinery concept, where bioethanol is upgraded to higher value-added chemicals (diethyl ether and 1,3-butadiene), provides an improved resilience and robustness against market price fluctuations by reducing economic loss by 140 MM$/a (17% IRR); (iii) the economic analysis favors biochemical conversion technologies for a small production/processing capacity, whereas the thermochemical conversion platform is favored for a relatively larger production capacity; and (iv) the microalgae-based biorefinery concept performed worse in terms of economics compared to the others, which is largely due to the cost of algae production and harvesting. In general, we recommend that a comprehensive economic risk analysis, using for example the Monte Carlo technique, should be an integral part of the conceptual design, development, and optimization of biorefineries to help improve their economic robustness in view of the competitive market for chemicals and fuels. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd [ABSTRACT FROM AUTHOR]

Global optimization for sustainable design and synthesis of a large-scale algae processing network under economic and environmental criteria is addressed. An algae processing network superstructure including 7800 processing routes is proposed. Based on the superstructure, a multiobjective mixed-integer nonlinear programming (MINLP) model is developed to simultaneously optimize the unit cost and the unit global warming potential (GWP). To efficiently solve the nonconvex MINLP model with separable concave terms and mixed-integer fractional terms in the objective functions, a global optimization strategy that integrates a branch-and-refine algorithm based on successive piecewise linear approximations is proposed and an exact parametric algorithm based on Newton's method. Two Pareto-optimal curves are obtained for biofuel production and biological carbon sequestration, respectively. The unit annual biofuel production cost ranges from $7.02/gasoline gallon equivalent (GGE) to $9.71/GGE, corresponding to unit GWP's of 26.491 to 16.52 kg CO2-eq/GGE, respectively. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3195-3210, 2014 [ABSTRACT FROM AUTHOR]

The design of thermally coupled distillation sequences explicitly including the possibility of divided wall columns (DWC) is described. A DWC with a single wall can be considered thermodynamically equivalent to a fully thermally coupled (FTC) subsystem formed by three separation tasks (a Petlyuk configuration in the case of three-component mixtures). It is shown how to systematically identify all the sequences of separation tasks that can produce configurations that include at least a DWC. Feasible sequences that explicitly include DWCs are enforced through a set of logical relationships in terms of Boolean variables. These logical relationships include as feasible alternatives from conventional columns (each column must have a condenser and a reboiler) to FTC systems (only one reboiler and one condenser in the entire system). A comprehensive disjunctive programming formulation for finding the optimal solution is presented. The model is based on the Fenske, Underwood Gilliland equations. However, the disjunctive formulation allows easily the use of any other shortcut, aggregated or even rigorous model without modifying much the structure of the model. Two illustrative examples illustrate the procedure. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1139-1159, 2013 [ABSTRACT FROM AUTHOR]

This article presents an algorithm developed to determine the appropriate sample size for constructing accurate artificial neural networks as surrogate models in optimization problems. In the algorithm, two model evaluation methods-cross-validation and/or bootstrapping-are used to estimate the performance of various networks constructed with different sample sizes. The optimization of a CO2 capture process with aqueous amines is used as the case study to illustrate the application of the algorithm. The output of the algorithm-the network constructed using the appropriate sample size-is used in a process synthesis optimization problem to test its accuracy. The results show that the model evaluation methods are successful in identifying the general trends of the underlying model and that objective function value of the optimum solution calculated using the surrogate model is within 1% of the actual value. © 2012 American Institute of Chemical Engineers AIChE J, 59: 805-812, 2013 [ABSTRACT FROM AUTHOR]

We address the problem of optimal design of reverse osmosis (RO)-based water treatment systems. A superstructure optimization method is proposed to solve the problem, where the superstructure for a RO system is structurally enhanced with additional features. We formulate the problem as mixed-integer nonlinear program which is solved to yield optimal results. A case study on desalination is considered in this work, and the numerical results obtained using our approach are validated using a commercial simulation tool. We further extend the problem by considering the effects of degradation of membrane performance over time and solve it by representing the problem as a two-stage stochastic program. This new approach is highly useful for identifying minimum cost robust designs for membrane-based water purification systems, which are especially important in desalination applications. © 2012 American Institute of Chemical Engineers AIChE J, 2012 [ABSTRACT FROM AUTHOR]