The importance of proper economic criteria and process modeling for single- and multi-objective optimizations

This paper provides an overview of the influences that different economic objectives have on the efficiencies of those optimal process designs obtained by using single- and multi-objective optimizations. Optimizations of monetary criteria, like the profit, lead to operationally and environmentally more efficient but economically less attractive designs than optimization of non-monetary economic objectives, like the internal rate of return. The net present value produces compromise designs with intermediate efficiencies and environmental impacts. These differences are significant only if the processes’ mathematical models are sufficiently accurate for establishing appropriate trade-offs between investment and cash flow. The Pareto curves obtained by different economic objectives vary regarding the maximum environmental impacts and in the intervals of the environmental indicators. The composed criteria that combine the economic and environmental indicators into one single objective produce smaller differences between optimum designs that are closer to those designs with minimum possible environmental impacts.     

Optimal Design and Economic Evaluation of Dividing‐Wall Columns

In an effort to reduce costs, a systematic optimization approach is proposed to address the energy consumption of dividing‐wall columns (DWCs). This iterative optimization procedure begins by minimizing the overall heat duty using an innovative objective function within a constrained design space. A sensitivity analysis is then carried out on the manipulated variables to obtain their optimal ranges. Optimal operating parameters are obtained through the evaluation of the total annualized cost (TAC). For the separation process of benzene/toluene/o‐xylene, the optimal DWC flow sheet exhibits a significant decrease in TAC when compared to conventional flow sheet optimum designs. The applied optimization method and sensitivity analysis have proven to produce results at the global optimum. This method is both practical and easily applied to other systems, even to systems with more than three components.     

Heat Transfer Phenomena of Power Law Fluids in Double‐Pass Heat Exchangers with Isoflux Conditions

A new design of conjugated heat transfer in double‐pass parallel‐plate laminar countercurrent operations of power law fluids under wall isoflux was investigated experimentally and theoretically. The analytical solutions were obtained with a superposition model by introducing an eigenfunction expansion in terms of a power series for the homogeneous part and an asymptotic solution for the inhomogeneous part. The influence of the power law index on the average Nusselt numbers with the various design and operating parameters is also delineated. The theoretical predictions of the experimental results are represented graphically. The heat transfer performance was considerably improved when compared with a single‐pass parallel‐plate heat exchanger (without inserting a solid separator sheet). Suitable adjustments of the solid separator sheet position can effectively enhance the heat transfer efficiencies for such a recycling double‐pass device, as compared with the efficiencies of single‐ and double‐pass devices.     

Synthesis and optimization of membrane cascade for gas separation via mixed‐integer nonlinear programming

Currently, membrane gas separation systems enjoy widespread acceptance in industry as multistage systems are needed to achieve high recovery and high product purity simultaneously, many such configurations are possible. These designs rely on the process engineer's experience and therefore suboptimal configurations are often the result. This article proposes a systematic methodology for obtaining the optimal multistage membrane flow sheet and corresponding operating conditions. The new approach is applied to cross‐flow membrane modules that separate CO₂ from CH₄, for which the optimization of the proposed superstructure has been achieved via a mixed‐integer nonlinear programming model, with the gas processing cost as objective function. The novelty of this work resides in the large number of possible interconnections between each membrane module, the energy recovery from the high pressure outlet stream and allowing for nonisothermal conditions. The results presented in this work comprise the optimal flow sheet and operating conditions of two case studies. © 2017 American Institute of Chemical Engineers AIChE J, 63: 1989–2006, 2017     

Steady‐state process optimization with guaranteed robust stability under parametric uncertainty

Interest in chemical processes that perform well in dynamic environments has led to the development of design methodologies that account for operational aspects of processes, including flexibility, operability, and controllability. In this article, we address the problem of identifying process designs that optimize an economic objective function and are guaranteed to be stable under parametric uncertainties. The underlying mathematical problem is difficult to solve as it involves infinitely many constraints, nonconvexities and multiple local optima. We develop a methodology that embeds robust stability constraints to steady‐state process optimization formulations without any a priori bifurcation analysis. We propose a successive row and column generation algorithm to solve the resulting generalized semi‐infinite programming problem to global optimality. The proposed methodology allows modeling different levels of robustness, handles uncertainty regions without overestimating them, and works for both unique and multiple steady states. We apply the proposed approach to a number of steady‐state optimization problems and obtain the least conservative solutions that guarantee robust stability. © 2011 American Institute of Chemical Engineers AIChE J, 2011     

Optimal design of batch‐storage network considering ownership

This article develops a model of multi‐national supply chain activities, which incorporates currency storage units to manage currency flows associated with activities such as raw material procurement, processing, inventory control, transportation, and finished product sales. The core contribution of this model is that it facilitates the quantitative investigation of the influence of macroscopic economic factors such as ownership on supply chain operational decisions. The supply chain system is modeled as a batch‐storage network with recycle streams. The supply chain optimization problem is posed with the objective of minimizing the opportunity costs of annualized capital investments and currency/material inventories, while taking into account the benefit to stockholders in the numeraire currency. The major constraints on the optimization are that the material and currency storage units must not be depleted. A production and inventory analysis formulation (the periodic square wave model) provides useful expressions for the upper and lower bounds and for the average levels of the currency and material inventory holdings. The expressions for the Kuhn‐Tucker conditions of the optimization problem are reduced to a subproblem that allows development of analytical lot‐sizing equations. The lot sizes of procurement, production, transportation, and financial transactions can be determined in closed form once the average flow rates are known. The key result we obtain is that optimal value of the economic order quantity changes substantially with variation in ownership, thus showing quantitatively that ownership structure does impact plant operation. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2418–2425, 2018     

Multi‐period planning,design, and strategic models for long‐term,quality‐sensitive shale gas development

In this work we address the long‐term, quality‐sensitive shale gas development problem. This problem involves planning, design, and strategic decisions such as where, when, and how many shale gas wells to drill, where to lay out gathering pipelines, as well as which delivery agreements to arrange. Our objective is to use computational models to identify the most profitable shale gas development strategies. For this purpose we propose a large‐scale, nonconvex, mixed‐integer nonlinear programming model. We rely on generalized disjunctive programming to systematically derive the building blocks of this model. Based on a tailor‐designed solution strategy we identify near‐global solutions to the resulting large‐scale problems. Finally, we apply the proposed modeling framework to two case studies based on real data to quantify the value of optimization models for shale gas development. Our results suggest that the proposed models can increase upstream operators’ profitability by several million U.S. dollars. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2296–2323, 2016     

Multi‐Phase‐Multi‐Size‐Group Model for the Inclusion of Population Balances into the CFD Simulation of Gas‐Liquid Bubbly Flows

A CFD model for the simulation of gas‐liquid bubbly flow is developed. In the model, the multi‐phase flow is simulated by an Eulerian‐Eulerian approach using several phase definitions (from 3 to 10). The bubble size distribution is simulated by a solution of the discretized population balance equation with coalescence and break‐up of bubbles. The number of the discretized population balance equations in the model is larger than the number of the phases used in the flow field simulation. A desired accuracy in the simulation can be achieved by choosing a suitable number of phases as a compromise between accuracy and computational cost. With this model, more detailed flow hydrodynamics and bubble size distribution can be obtained. The model was tested with different operating conditions and for different numbers of dispersed phases in a bubble column, and was verified with a bubble size distribution obtained experimentally.     

Using NSGA‐II and TOPSIS Methods for Interior Ballistic Optimization Based on One‐Dimensional Two‐Phase Flow Model

In order to meet the design demands of new gun systems or new types of projectiles, the interior ballistic charge design seems especially important. In this paper, a one‐dimensional two‐phase flow model is presented. The model describes the transient combustion of granular propellants in a gun, and pressure waves are considered as an objective. This study adopts a hybrid method to solve the problem. In the first stage, the non‐dominated sorting genetic algorithm (NSGA‐II) with “a “filter” is employed to approximate a set of Pareto‐optimal solutions. In the subsequent stage, a multi‐attribute decision‐making (MADM) approach is adopted to rank these solutions from the best to the worst. The ranking of Pareto‐optimal solutions is based on the technique ordered preference by similarity to ideal solution (TOPSIS) method. In TOPSIS method each objective needs a corresponding weight coefficient, and a practical problem is introduced. Both the entropy method and linear analysis method are adopted to get two sets of weights for the objectives, respectively. The two pairs of final, best compromise solutions are compared for satisfying the designer’s aim. For the analysis of the results, a two‐phase flow interior ballistic model for design optimization is established, and the hybrid approach could get a reasonable design scenario.     

Microalgal phycocyanin productivity: strategies for phyco‐valorization

Phyco‐valorization is the exploitation of microalgae and microalgal chemicals as valuable products. This paper discusses the optimization of microalgal bioreactor‐based systems for C‐phycocyanin pigment production. Various aspects contributing to system development and enhancement of phycocyanin productivity are described. A wide range of potential microalgal species have been identified for phycocyanin production; the selection of a species for mass culturing can be determined by desired bioreactor trophic mode and symbiotic relations. Research has demonstrated that species amenability to local lighting and climatic conditions, and to variations in bioreactor substrate concentrations and operational parameters, have significant impact on phycocyanin production. The simultaneous optimization of all factors contributing to system productivity may be accomplished efficiently through process modelling. A summary of established models for microalgal phycocyanin production is presented. A suggested strategy for increasing economic viability of phycocyanin production systems is their application in integrated resource recovery. Through the incorporation of phycocyanin productivity optimization principles within a phycoremediation process, the valorization of waste resources may be achieved. The simultaneous economic potential and environmentally‐forward concept of phyco‐valorization through phycocyanin production is a promising application of microalgal biotechnology awaiting further development for industrial implementation. © 2015 Society of Chemical Industry     

Inverse exergo‐ökologisch‐ökonomische Prozessanalyse von Abgasbehandlungsprozessen

A novel process analysis methodology called IEEA (Inverse Exergoeconomical and Environmental Analysis) is introduced for the first time, which combines economic, energetic, and environmental analyses. The approach is derived basically from classical approaches by using ecological indicators and an inverse economic analysis. By combining these with the results of an exergetic analysis, strategies for a process improvement can be developed systematically through the use of key indicators. The IEEA is applied to processes for the treatment of hydrogen sulfide containing sour gases, which appear during coking processes of mineral coal. It can be shown that a high benefit can be achieved by performing the proposed IEEA improvement steps.     

Solving Multi‐objective MILP Problems in Process Synthesis using the Multi‐Criteria Branch and Bound Algorithm

The paper briefly describes the problem of process synthesis in the area of chemical engineering, and suggests its formulation as a Multi‐Objective Programming problem. Process synthesis optimization is usually modeled as Mixed Integer Linear Programming (MILP) or Mixed Integer Non‐Linear Programming (MINLP) with an economic objective function. We claim that incorporating more criteria (e.g., environmental criteria) in this kind of combinatorial optimization problem offers the decision makers the opportunity to refine their final decision by examining more than one solution (a set of efficient or Pareto optimal solutions instead of one optimal solution). For solving the multi‐objective process synthesis problem, an improved version of the Multi‐Criteria Branch and Bound (MCBB) algorithm, which has been developed by the same authors, is used. MCBB is a vector maximization algorithm capable of deriving all efficient points (supported and unsupported), for small and medium sized Multi‐Objective MILP problems. The application of MCBB in two examples from process synthesis is also presented.     

Multiobjective optimization of multiproduct batch plants scheduling under environmental and economic concerns

In batch process scheduling, production trade‐offs arise from the simultaneous consideration of different objectives. Economic goals are expressed in terms of plant profitability and productivity, whereas the environmental objectives are evaluated by means of metrics originated from the use of life cycle assessment methodology. This work illustrates a novel approach for decision making by using multiobjective optimization. In addition, different metrics are proposed to select a possible compromise based on the distance to a nonexistent utopian solution, whose objective function values are all optimal. Thus, this work provides a deeper insight into the influence of the metrics selection for both environmental and economic issues while considering the trade‐offs of adopting a particular schedule. The use of this approach is illustrated through its application to a case study related to a multiproduct acrylic fiber production plant, special attention is put to the influence of product changeovers. © 2010 American Institute of Chemical Engineers AIChE J, 57: 2766–2782, 2010     

Modular methanol manufacturing from shale gas: Techno‐economic and environmental analyses of conventional large‐scale production versus small‐scale distributed,modular processing

This article presents comparative techno‐economic and environmental analyses of four representative shale gas monetization options, namely, conventional shale gas processing, large‐scale methanol manufacturing, modular methanol manufacturing with shale gas supplied by pipelines, and modular methanol manufacturing with consideration of plant relocation. We first present shale gas supply models for the four gas monetization options. Next, the process designs for shale gas processing and methanol manufacturing from shale gas are described. We develop detailed process simulation models for shale gas processing and methanol manufacturing with different scales using raw shale gas extracted from the Marcellus, Eagle Ford, and Bakken shale plays. On this basis, techno‐economic analyses and environmental impact analyses are conducted for the four shale gas monetization options to systematically compare their economic and environmental performances based on the same conditions. The results show that modular methanol manufacturing is more economically competitive than conventional shale gas processing, although it leads to higher environmental impacts. Besides, modular methanol manufacturing is better than large‐scale methanol manufacturing for raw shale gas produced from distributed, remote wells from both economic and environmental perspectives. © 2017 American Institute of Chemical Engineers AIChE J, 64: 495–510, 2018     

Sustainable design and synthesis of algae‐based biorefinery for simultaneous hydrocarbon biofuel production and carbon sequestration

The superstructure optimization of algae‐based hydrocarbon biorefinery with sequestration of CO₂ from power plant flue gas is proposed. The major processing steps include carbon capture, algae growth, dewatering, lipid extraction and power generation, and algal biorefinery. We propose a multiobjective mixed‐integer nonlinear programming (MINLP) model that simultaneously maximizes the net present value (NPV) and minimizes the global warming potential (GWP) subject to technology selection constraints, mass balance constraints, energy balance constraints, technoeconomic analysis constraints, and environmental impact constraints. The model simultaneously determines the optimal decisions that include production capacity, size of each processing unit, mass flow rates at each stage of the process, utility consumption, economic, and environmental performances. We propose a two‐stage heuristic solution algorithm to solve the nonconvex MINLP model. Finally, the bicriteria optimization problem is solved with ε‐constraint method, and the resulting Pareto‐optimal curve reveals the trade‐off between the economic and environmental criteria. The results show that for maximum NPV, the optimal process design uses direct flue gas, a tubular photobioreactor for algae growth, a filtration dewatering unit, and a hydroprocessing pathway leading to 47.1 MM gallons of green diesel production per year at $6.33/gal corresponding to GWP of 108.7 kg CO₂‐eq per gallon. © 2013 American Institute of Chemical Engineers AIChE J, 59: 1599–1621, 2013     

Multicriteria Optimization of a High‐Yield Pulping Process

This work deals with the application of a multicriteria optimization technique for the optimization of a jack pine pulping process, which has a large number of input and output variables. The optimization process is mainly comprised of two steps: reduction of the volume of the search space and determination of good compromise solutions. In the first step, the Pareto domain is approximated by a large number of possible solutions. The optimization technique uses the concept of preference, indifference and veto to determine the best solutions. Results show that an optimal solution zone can be defined and zones of decreasing preference can be drawn.     

Optimization of a High Temperature PEMFC micro‐CHP System by Formulation and Application of a Process Integration Methodology

A 1 kWe micro combined heat and power (CHP) system based on high temperature proton exchange membrane fuel cell (PEMFC) technology is modeled and optimized by formulation and application of a process integration methodology. The system can provide heat and electricity for a single‐family household. It consists of a fuel cell stack, a fuel processing subsystem, heat exchangers, and balance‐of‐plant components. The optimization methodology involves system optimization attempting to maximize the net electrical efficiency, and then by use of a mixed integer nonlinear programming (MINLP) problem formulation, the heat exchange network (HEN) annual cost is minimized. The results show the high potential of the proposed model since high efficiencies are accomplished. The net electrical efficiency and total system efficiency, based on lower heating value (LHV), are 35.2% and 91.1%, respectively. The minimized total annual cost of the HEN is $8,147 year^–1.     

Optimization‐driven design of dies for profile extrusion: Parameterization,strategy, and performance

A computational design optimization environment is developed, handling, for the first time, streamline dies used for profiles in unplasticized polyvinyl chloride (uPVC) having multiple complex features, as well as simpler designs. Die cavity cross sections are described by planar contours, such as the cutting paths for wire electrical discharge machining of the plates from which streamline profile dies are constructed. Contours are parameterized using key points, and by joining the contours with ruled surfaces, the three‐dimensional geometry can be reconstructed. For the optimization a developed flow analysis on each die cross section is used with the avoid‐cross‐flow strategy. Cross sections are partitioned and the die is balanced to obtain the required flow rate through each. A robust and efficient parallel decoupled optimization strategy is developed. In application to a uPVC window profile, five cross sections were optimized. The number of design variables on each ranged from 2 to 46, and the cross section optimizations converged within one to seven cycles. Compared with the work of an experienced designer making manual changes to the computer‐aided design model, guided by computational fluid dynamics analyses, the design quality was comparable or better and computational demands similar; however, the time required from the designer was reduced seven times. POLYM. ENG. SCI., 2013. © 2012 Society of Plastics Engineers