Systematic MultiObjective Life Cycle Optimization Tools Applied to the Design of Sustainable Chemical Processes

C H A P T E R

17 Systematic MultiObjective Life Cycle Optimization Tools Applied to the Design of Sustainable Chemical Processes Gonzalo Guille´n-Gosa´lbez1, Andre´s Gonza´lez-Garay2, Phantisa Limleamthong2, A´ngel Gala´n-Martı´n1, Carlos Pozo2 1

Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zu¨rich, VladimirPrelog-Weg 1, Zu¨rich, Switzerland; 2Department of Chemical Engineering, Centre for Process System Engineering, Imperial College London, South Kensington Campus, London, United Kingdom

O U T L I N E 1. Introduction

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2. MultiObjective Life Cycle Optimization of Chemical Products and Processes 2.1 Costing of Chemical Processes 2.2 Life Cycle Assessment 2.2.1 Goal and Scope Definition 2.2.2 Inventory Analysis 2.2.3 Life Cycle Impact Assessment 2.2.4 Interpretation 2.3 Chemical Process Modeling 2.3.1 Equation-Oriented Versus SimulationOptimization Approaches

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2.3.2 MultiObjective Optimization 2.3.3 MOO Solution Methods 2.3.4 Methods for Selecting the Most Preferred Solution

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3. Areas of Application 3.1 Supply Chain Optimization 3.2 Process Flowsheet Optimization 3.3 Environmental Assessment of Chemicals

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4. Conclusions

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References

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1. INTRODUCTION Over the past few decades, sustainability has become a major concern worldwide, prompting governments to invest in new solutions to meet the growing demand of food, energy, and water in a sustainable manner. In this context, industrial sustainability is understood as “an approach towards the selection of clean technologies that minimize the consumption of raw materials & energy and the generation of pollutants, while renewable resources are maximized as inputs to produce recyclable or biodegradable products” [1]. The incorporation of environmental concerns into process design and operations has posed many challenges for industry, which must provide high quality products and services while at the same time looking for alternative technologies that minimize the dependency on raw materials, energy, labor, and waste [1].

Sustainable Nanoscale Engineering https://doi.org/10.1016/B978-0-12-814681-1.00017-5

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Copyright © 2020 Elsevier Inc. All rights reserved.

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Following this general trend, the chemical industry is at present seeking to become more sustainable while maintaining its profit margins high. This can be accomplished through the design of greener chemicals as well as the optimization of chemical processes to ultimately reduce emissions and waste generation as well as energy and water consumption. With regard to the first approach (i.e., greener chemicals), the principle of “Green Chemistry” offers a practical framework for reducing environmental burdens at the molecular scale [2]. “Green Engineering,” alternatively, puts emphasis on how to attain the sustainability at the process and system levels via the development of technology [2]. In this general context, the design of more environmentally benign materials, products, processes, and systems could be performed beyond the baseline of engineering quality and safety specifications through the adoption of 12 principles that take environmental, economic, and social criteria into consideration [2]. Both approaches, microscopic (product design) and macroscopic (process design), comprise three major steps: (i) the assessment of environmental burdens of products and processes of interest, (ii) the benchmarking of their environmental performance and establishment of improvement targets, and (iii) the identification of solutions that improve the sustainability level of these systems. A number of systematic methodologies have been recently developed to assist in these three fundamental steps, which were reviewed in the work by Nikolopoulou and Ierapetritou [3] and are discussed in more detail next. Among the tools available for the environmental assessment of products, processes, and services (step one as discussed above), life cycle assessment (LCA) has become the preferred choice, gaining worldwide acceptance as a technique that systematically analyzes the environmental impacts of a product or service from cradle-to-grave. Hence, in LCA the product/process is evaluated throughout its life cycle, starting from resources extraction, through manufacturing, and ending with waste disposal [4]. LCA quantifies the system’s inventory, i.e., energy, material usages, and emissions and waste generated, to assess the corresponding environmental impacts (i.e., to measure the system’s environmental performance) and finally identify potential environmental improvements [5]. These advantageous features of LCA enable environmental management in both corporate and public sectors to become more tangible, as described in the ISO 14040 standards [6]. Overall, LCA has found many applications in process selection, design, and optimization, becoming an alternative approach to identify clean technologies that has facilitated the widespread application of sustainability principles in industry [7e9]. With regard to step two, and particularly focusing on the area of product and process design, it is common in the chemical industry to find alternative technologies with similar properties and applications. In this context, it is necessary to compare them against each other according to several criteria, set out beforehand, to ensure that the best-performed solution is chosen. This is essentially a characteristic of sustainability-related problems, in which several criteria are considered simultaneously, giving rise to a so-called multicriteria decision-making problem. Wang et al. [10] reviewed various methods for multi-criteria decision analysis, including the weighted sum method (WSM), the analytical hierarchy process, the technique for order preference by similarity to ideal solutions, and the elimination and choice translating reality, among others. In these methods, decision-makers select the best option following a ranking based on aggregated scores obtained by assigning subjective weights of importance to each criterion [10]. The way in which weights are handled varies significantly from one approach to another, thereby leading to different final results [11]. All of these methods provide as main outcome a ranking of alternatives; unfortunately, this ranking provides little insight (if any) into the potential sources of environmental inefficiencies, neither does it offer quantitative guidelines on how to enhance the level of sustainability of a product or service. In this regard, we note that the majority of approaches toward sustainability are based on a “direction to target” approach [12], which establishes an improving direction to the sustainability target without clear quantitative guidelines on how to achieve it. “Distance to target” approaches, on the contrary, distinctively offer more practical and meaningful guidelines by measuring the magnitude toward (or away from) sustainability, thereby enabling the definition of useful quantitative targets [13]. With regard to step three (i.e., finding solutions with improved sustainability level), the growing increase in sustainability awareness together with the adoption of more stringent regulations have motivated the integration of sustainability criteria into optimization tools, the latter originally developed to maximize profit as unique criterion. In the context of the chemical industry, moving toward more sustainable chemical processes (those that are cost-effective, energy efficient, consume less resources, emit less emissions, and overall show smaller environmental impacts) requires practical process retrofits and modifications. These can be underpinned by the use of heuristics, the development of physical insightsdcommonly linked to thermodynamicsdand, more often, by optimization tools [8]. Notably, due to recent advances in optimization theory and software applications, mathematical programming has gained wider interest in sustainability problems. This approach comprises three major steps. The first step is to establish a superstructure of alternatives from which the optimal solution is chosen. The second step is to formulate a mathematical problem based on the equations that describe the unit operations and process topology in the superstructure, where equipment units, interconnectivity between them, and process

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constraints are all together considered simultaneously. The optimization models formulated in this approach often contain discrete and continuous variables, employed for selecting the process configuration as well as operating conditions, respectively. Furthermore, these models include both linear and nonlinear constraints, leading to mixed-integer nonlinear programming problems (MINLP) [14] whose solution dictates the optimal flowsheet configuration and process conditions. The recent trend is to couple optimization models with LCA so as to identify solutions that reduce simultaneously the total cost and environmental impact of products and processes. This approach is described in more detail in the ensuing sections.

2. MULTIOBJECTIVE LIFE CYCLE OPTIMIZATION OF CHEMICAL PRODUCTS AND PROCESSES The multiobjective life cycle optimization framework has gained wide acceptance in the process systems engineering community as a fundamental tool to assist in the design and operation of more sustainable products and processes. In essence, this combined method integrates LCA with optimization tools, where the former is employed to assess alternatives from an environmental viewpoint, while the latter generates such alternatives and identifies the best ones in a systematic manner. Incorporating sustainability principles into process design is challenging, as it requires the simultaneous consideration of a wide range of economic, environmental, and social criteria in problems that are per se quite complex [15,16]. Defining adequate sustainability metrics in the form of key performance indicators (KPIs) is essential for effectively supporting the decision-making process [17,18]. The need to consider various KPIs simultaneously leads to complex multicriteria decision-making problems, in which identifying the best decisions over a wide range of potential alternatives becomes critical. In chemical process design, the objective is to find the best option in terms of these criteria through their evaluation and simultaneous optimization. We discuss next the main ingredients of the life cycle optimization approach.

2.1 Costing of Chemical Processes The literature around the economic evaluation of chemical processes is substantial, with many methodologies available to serve this purpose. The economic objective during the design stage is usually related to the minimization of the costs or the maximization of the profit, using metrics such as payback time, return on investment, net present value, or discounted cash flow rate of return [19]. The generation of any of these economic indicators involves the evaluation of capital, fixed and variable costs, as well as the revenues of the process. Generally, all the terms applied are calculated on an annual basis. Capital costs are the costs inherent to the equipment and its installation. There are three main methods to calculate the capital costs: power law, cost factors, and detailed estimates. Given their simplicity, the first two methods are the most widely applied in early design stages; unfortunately, they lead to errors of 30%e50%, which are nevertheless considered admissible during this early stage. The total annualized capital cost is determined considering the annual capital charge (ACC) ratio. Fixed costs of production are independent from the rate of production and include labor, maintenance, land, insurance, interest payments, overhead, license fees, and royalties. Variable costs of production are dependent on the rate of production and account for consumption of raw materials, utilities, consumables, and waste disposal. Finally, the total annualized cost, obtained as the sum of the variable costs, fixed costs, and ACC, is one of the most applied economic indicators during the evaluation of chemical projects. TAC ¼ FCOP þ VCOP þ ACC

(17.1)

Revenues are generated from sales of main products and by-products and can be used to determine the profit together with the annualized cost.

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FIGURE 17.1 Phases of the life cycle assessment methodology [20].

2.2 Life Cycle Assessment LCA is an environmental management tool that quantifies the environmental loads and the corresponding environmental impacts of a product, process, or activity throughout its whole life cycle to assess opportunities for environmental improvements [5]. The International Organization for Standardization (ISO) has developed the framework and guidelines for a practical use of the LCA methodology, issuing a series of standards documents in the 14040 series [6]. In general, LCA comprises four major phases as following: • • • •

Goal and scope definition Inventory analysis Life cycle impact assessment (LCIA) Life cycle interpretation Fig. 17.1 displays the main steps in the LCA methodology, described in more detail next.

2.2.1 Goal and Scope Definition The first LCA step sets up the explicit goal of the LCA, often corresponding to the assessment and comparison of the environmental performance of alternative processes, ultimately aiming at providing guidelines for system improvements to minimize environmental impacts [5]. Consequently, the scope of the study, i.e., system boundaries, functional unit, assumptions and limitations, and selected impact categories, will be defined bearing this goal in mind. There are several ways to define the system boundaries. For example, cradle-to-grave implies the full product life cycle from resource extraction to waste disposal. On the other hand, cradle-to-gate encompasses all the activities from resources extraction to the factory gate (i.e., excluding transportation to final customers), while cradle-to-cradle is a specific kind of cradle-to-grave where the waste products are recycled instead of disposed in the final step. The functional unit allows for a fair comparison between system alternatives; it refers to an equivalent amount of goods or services delivered by the product systems and, therefore, provides a standardization that enables alternative goods or services to be compared and analyzed [21]. 2.2.2 Inventory Analysis This phase quantifies the inventory flows, from and to the environment, associated with a product system [22]. It makes use of the compilation of available data and the calculation of material and energy balances to quantify relevant resources (i.e., water, raw material, and energy) and emissions related to the functional unit defined in the first step [7]. Due to the need for data documentation, several public standard databases have been developed and are available for use, such as SPINE, developed by the Center for Environmental Assessment of Product and Material Systems [23], and ECOINVENT, developed by the Swiss Center for Life Cycle Inventories [24], among others. These can be accessed directly or via software packages with friendly user interfaces, such as Gabi, SimaPro, Umberto, or, more recently, open source initiatives such as openLCA.

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2.2.3 Life Cycle Impact Assessment The LCIA converts the resources and emissions, in other words, the environmental burdens specified in the life cycle inventory of a system and previously quantified in the inventory analysis phase, into their potential environmental impacts. There are a number of LCIA methods available classified based on the impact categories and characterization factors they implement [9]. 2.2.3.1 Classification The environmental burdens are first aggregated according to the impact categories to which they are linked, specified in the goal of the study. Widely used impact categories include climate change, stratospheric ozone depletion, photo-oxidant formation (smog), eutrophication, acidification, water use, and noise, among others. Environmental burdens are often translated into a common impact category to enable comparisons between them [25]. For instance, emissions of CO2, CH4, VOCs, and other greenhouse gases can be expressed as CO2-equivalent emissions to evaluate the overall global warming potential (GWP), which takes all sources of greenhouse gases into account [7]. In addition, according to the ISO methodology, the impact categories can be grouped into three areas of protection (AoPs): resources use, human health consequences, and ecological consequences [26]. 2.2.3.2 Characterization In the characterization step, the potential contribution of each environmental burden to each impact category is evaluated. This can be done by multiplying the inventory entry (i.e., emission, waste, or resource) by its characterization factor. The characterization factors refer to potential environmental impacts per unit of emission of a given substance and, therefore, are specific to a particular substance [9]; they are in turn computed considering the impact category to which a substance could potentially contribute [27]. A set of characterization factors determined following different approaches are available in the literature, most of which were already implemented in databases and software packages for a wide range of indicators, like the Eco-indicator 99 [28], CML [29], and Recipe 2016 [30]. The mathematical expression used to calculate a category indicator is shown below. X Sj ¼ Qj;i mi cj (17.2) i

where Sj is the indicator for impact category j, mi refers to the life cycle inventory of substance i, and Qj,i is the characterization factor of substance i that contributes to impact category j [26]. 2.2.3.3 Normalization Normalization is applied when practitioners wish to compare products across impact categories, or even across AoPs, to prioritize or to resolve trade-offs between product alternatives [26]. This optional step also identifies which impact categories are minor contributors to the overall environmental problem and can, therefore, be excluded from the evaluation. The normalization is carried out by dividing the impact category indicators by a reference value as follows: Sj Nj ¼ cj (17.3) Rj where j denotes the interested impact category, Nj is the normalized indicator, Sj is the category indicator obtained in the characterization step, and Rj is the reference value of impact category indicator. When selecting the reference value, the following attributes should be taken into consideration: spatial scale, temporal scale, a defined system (e.g., a region or an economic sector), and a per capita basis. 2.2.3.4 Valuation The valuation is the final step in LCIA, whereby the relative importance of different impacts is incorporated in the assessment by using weighting factors assigned to each impact and further aggregating them into a single environmental indicator [31]. These weighted expressions are usually represented as a linear equation as below: X X EI ¼ wnj Nj or EI ¼ wsj Sj (17.4) j

j

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where EI denotes the overall environmental impact indicator, wnj is the weighting factor for normalized impact category j, and wsj is the weighting factor for impact category j when the normalization step is skipped in the characterization phase [26]. This valuation step is regarded as the most controversial element of LCA due to its high degree of subjectivity in determining the significance of different impacts [7]. Hence, both normalization and weighting are regarded as optional steps that can be omitted when found appropriate. 2.2.4 Interpretation The interpretation, carried out in the final phase of the LCA methodology, aims to identify potential improvements in environmental performance of a particular system. According to the ISO methodology, these may involve improvements, innovations, the identification of significant stages or issues in the life cycle contributing to the impacts, a sensitivity analysis, and final recommendations [7]. The interpretation phase requires the evaluation of the system alternatives according to a set of indicators. Unfortunately, due to the inherent trade-offs found between impacts in many applications, it is very unlikely that one single alternative will perform better than the rest in all of the categories simultaneously. The standard LCA application ends with the discussion of these set of alternative cases, which are defined beforehand, and their inherent trade-offs. To enlarge the LCA capabilities, this methodology can be integrated with optimization tools that can explore in a systematic manner a vast number of solutions in short CPU times. This integration between LCA and optimization is described in detail next after a brief discussion on the main modeling tools used in process systems engineering.

2.3 Chemical Process Modeling Process modeling plays a key role in underpinning the design and operation of new and existing chemical processes. We discuss some general modeling issues next before describing how modeling tools can be integrated with LCA. 2.3.1 Equation-Oriented Versus Simulation-Optimization Approaches Two main options emerge in process modeling: equation-oriented approaches and simulation-based approaches. The equation-oriented approach typically establishes a superstructure of alternatives on the basis of which a mathematical problem containing discrete and continuous variables is formulated; this model is then optimized for selecting the optimal process configuration and operating conditions [14]. The simultaneous equationoriented approach often leads to nonconvex MINLP formulations that can be solved with state-of-the-art software packages (e.g., GAMS, AIMMS, LINDO, MATLAB, etc.). To avoid very complex MINLPs potentially leading to prohibitive CPU times, the modeling is simplified by using less accurate shortcut methods showing better numerical performance [32,33]. This helps the calculations but at the expense of sacrificing to some extent the accuracy of the results. In contrast, in the simulation-based approach, modular sequential models based on detailed equations of unit operations are solved; this leads to more accurate results but the downside is that standard optimization algorithms cannot be then applied in a straightforward manner, thereby compromising the optimality of the final solutions found. Here, the modeling and optimization tasks are decoupled from each other by simulating the process with a detailed model that is optimized with an external algorithm. While the formulation of these problems is still nonconvex and nonlinear, the decoupling of the modeling and optimization tasks enables the use of tailored initialization techniques and solution algorithms that facilitate the convergence of the flowsheet; the optimization task, however, becomes more challenging as it cannot have direct access to the detailed explicit equations [34,35]. The recent trend is now to replace the simulation model by a surrogate model to expedite the calculations, using to this end a variety of regression methods including kriging, neural networks, and splines [36]. 2.3.2 MultiObjective Optimization The inclusion of sustainability criteria into product and process optimization naturally leads to multiobjective optimization (MOO) problems, in which the environmental and economic criteria are simultaneously optimized

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FIGURE 17.2 Pareto curve.

[8]. The optimization problem with sustainability objectives can be formulated as a multiobjective mixed-integer nonlinear problem (moMINLP) as follows [20]: min

Uðx; yÞ ¼ ff1 ðx; yÞ; f2 ðx; yÞg

s:t:

hðx; yÞ ¼ 0

x;y

gðx; yÞ  0

(M.1)

x˛<; y˛f0; 1g where f1(x,y) refers to the total cost and f2(x,y) represents the environmental impact. Note that, when possible, social indicators shall be also defined as additional objectives. Unlike single-objective optimization, MOO generates a set of optimal solutions known as Pareto set, which represent the optimal trade-off between the different objectives in the MOO problem. These are nondominated solutions, implying that no other solution would have better performance in an objective without worsening at least one of the others. The Pareto optimality concept is displayed in Fig. 17.2, where two objectives, cost and environmental impact, are considered. The points lying on the Pareto curve are Pareto optimal [7,20]. Finding feasible solutions under the Pareto curve is impossible, because Pareto optimal solutions cannot be dominated; solutions above the curve, however, do exist, but are suboptimal. From this set of Pareto solutions, decision-makers should select the best one according to their particular preferences as well as the applicable legislation [20]. 2.3.3 MOO Solution Methods The most commonly used algorithms for generating the Pareto optimal solutions of an MOO problem are the weighted sum and epsilon constraint methods. In the WSM, a weighted sum of objectives is optimized by exploring the space of possible weights [37]. In the epsilon constraint method, one objective is kept as main objective function, while the others act as auxiliary constraints which impose bounds on the main objective [37,38]. 2.3.3.1 Weighted Sum Method The WSM solves an auxiliary SOO model (M.2), derived from the original MOO problem, that optimizes a linear weighted sum of the original objectives previously normalized [37].   min WS ¼ ð1 lÞf 1 ðx; yÞþ .þ lf n ðx; yÞ x;y

s:t:

hðx; yÞ ¼ 0 gðx; yÞ  0

(M.2)

0l1 x; l˛<; y˛f0; 1g 2.3.3.2 Epsilon Constraint Method The ε-constraint method, first introduced by Haimes et al. [38], transforms the original multiobjective model into a set of single-objective problems, where the optimization is carried out considering one objective function at a time

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while defining the other objectives as constraints bounded by some allowable levels, i.e., ε values. The optimization problem, as shown below, is solved many times for different ε values to generate the entire Pareto optimal set. min

EC ¼ f1 ðx; yÞ

s:t:

fo ðx; yÞ  εo

x;y

o ¼ 2; .; n

hðx; yÞ ¼ 0

(M.3)

gðx; yÞ  0 x˛<; y˛f0; 1g 2.3.4 Methods for Selecting the Most Preferred Solution Once the Pareto solutions are generated, decision-makers need to identify the best ones considering different criteria. Here, the multicriteria methods described previously can be applied to assist in the analysis of the inherent trade-offs.

3. AREAS OF APPLICATION In this section, we present three different application domains of LCA principles combined with optimization tools, an approach originally introduced by Azapagic and Clift [7] and reviewed elsewhere [8,9,39]. The first example deals with the optimization of a hydrogen supply chain for vehicles use at the macroscale level. The second problem addresses flowsheet optimization in benzene production and deals with the mesoscale level. The third example, at the molecular level, covers the life cycle impact assessment of molecules.

3.1 Supply Chain Optimization With the rapid worldwide globalization, supply chain optimization has gained interest in the private and public sectors. Substantial work was carried out in the areas of biomass supply chains [40,41] and petrochemicals supply chains [42e44] following the life cycle optimization framework. Here, we study the optimal design of a hydrogen supply chain for vehicle use in United Kingdom considering both economic and environmental concerns simultaneously [45]. The adoption of hydrogen fuels has emerged as a promising alternative to reduce the environmental impact of today’s fossil fuelebased transportation sector. To assist in the design of hydrogen supply chains, the authors presented a bi-criterion MILP that systematically determines the optimal hydrogen supply chain network configuration and associated planning decisions to satisfy a given hydrogen demand considering simultaneously the total cost and life cycle impact (Fig. 17.3). The superstructure includes a set of plants that produce hydrogen and a set of potential storage facilities, where hydrogen is stored before being delivered to customers via various transportation options. Three hydrogen production technologies were considered, namely steam methane reforming, coal gasification, and biomass gasification, while hydrogen can be stored via liquid hydrogen storage and compressed gas storage. For hydrogen transportation, four options were considered, i.e., hydrogen tanker trucks, liquid hydrogen railway tank cars, compressed-gaseous hydrogen tube trailers, and compressed-gaseous hydrogen railway tube cars. The MILP network model accounts simultaneously for the optimization of the cost and environmental performance of the hydrogen network. The economic objective corresponds to the total discounted cost, which includes both capital and operating costs given by the production, storage, and transportation facilities of the network. The environmental performance was quantified through the LCA methodology focusing on climate change measured via the Eco-indicator 99. To solve the bi-criterion problem, the authors applied the epsilon constraint method combined with a bi-level decomposition algorithm that expedites the calculations, which are computationally demanding given the size of the problem (e.g., for a problem with 10 periods, the model contains 21,160 binary variables, 1880 discrete variables, 25,396 continuous variables, and 70,976 constraints). The interested reader is referred to [45] for further details on the bi-level algorithm.

3. AREAS OF APPLICATION

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FIGURE 17.3 Problem statement of the hydrogen case study.

The results of the model are shown in the Pareto set of solutions depicted in Fig. 17.4. Note that there is a clear trade-off between cost and damage to human health due to climate change, where each solution entails a specific structure of the hydrogen supply chain. Specifically, two alternatives are shown in Fig. 17.4, which correspond to the extreme solutions. In the first alternative A (i.e., minimum cost solution), steam reforming is selected to produce hydrogen. Moving toward solutions with lower environmental impact requires producing hydrogen exclusively from biomass, with the solution with lowest damage to human health (solution E) using only compressed hydrogen gas in addition to biomass. Overall, results suggest that replacing steam reforming by biomass gasification is a promising alternative to reduce the environmental impact; meanwhile, substituting liquefied hydrogen by compressed gas hydrogen as storage technology would significantly increase the total costs without attaining significant environmental benefits. The algorithm, in turn, provides the optimal network configuration, including specific locations for production and storage facilities and associated material flows between them.

3.2 Process Flowsheet Optimization The second example illustrates how LCA can be integrated into process design. Guille´n-Gosa´lbez et al. [20] incorporated LCA principles into MINLP models to address the structural optimization of sustainable chemical process flowsheets. The well-known hydrodealkylation of toluene (HDA) process was optimized following this approach, where cost and environmental impact were the objectives minimized (Fig. 17.5). The superstructure considers a toluene fresh feed that reacts with a hydrogen stream that can be further purified using a membrane separator. The exothermic reaction can be conducted in a plug-flow reactor operating either adiabatically or isothermally, where the latter is more expensive due to heat removal. Different alternatives are considered for the vapor stream exiting the flash separator located downstream the reactor, including the use of an absorber to recover the benzene lost in the flash separator, a purge to avoid methane accumulation in the system, or a membrane separator that decreases the hydrogen loss in the purge stream. A portion of the liquid stream exiting the flash is sent to the liquid separation system, where hydrogen and methane are removed using either a stabilizing column or a second flash separator. A distillation column yields a benzene stream with the desired purity of 99.97%. The bottom stream, containing primarily toluene, can be split in a flash separator or in a column. The purge streams containing methane and diphenyl by-product streams are combusted to produce steam that can be used in other parts of the plant. The superstructure for this HDA process was modeled as an MINLP similarly as in [46]. Simplified models such as Raoult’s law for phase equilibrium, the FenskeeUnderwood equation for distillation columns, and the Kremser equation for the absorber were used. The resulting mathematical formulation simultaneously accounts for the minimization of the environmental impact and cost, where the former is measured through the Eco-indicator 99. The resulting moMINLP can be solved by standard techniques for MOO, such as the ε-constraint method [38]. The solution to the problem consists of a set of Pareto optimal flowsheet configurations and corresponding operating conditions (Fig. 17.6). All the Pareto optimal designs use the isothermal reactor, the stabilizing column, and the second flash, but they differ in the use of the membrane in the input stream and in the methane purge. Notably, the minimum cost solution avoids the membrane in the input stream but uses the one placed in the methane

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Pareto set of optimal hydrogen supply chain solutions and minimum cost and environmental impact solutions.

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FIGURE 17.4

3. AREAS OF APPLICATION

FIGURE 17.5

FIGURE 17.6

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Superstructure for the hydrodealkylation (HDA) process synthesis problem.

Trade-off solutions of the hydrodealkylation process superstructure [20].

purge, while the converse occurs in the minimum impact design. The MINPL algorithm, therefore, determines the optimal plant topology, equipment sizes, and operating conditions for a given economic and environmental performance, offering a set of design alternatives representing the optimal trade-off between both criteria.

3.3 Environmental Assessment of Chemicals The last example deals with streamlined LCA applied to chemicals for potential applications in molecular design and product development. The motivation for this work lies in the fact that LCA studies require large amounts of data hard to collect in practice, particularly considering the value chains in the chemical industry encompassing hundreds of technologies. Producing accurate estimates of environmental impact for chemicals, however, is critical for a proper assessment of a wide range of products and processes. To simplify LCA calculations, streamlined LCA methods can be applied [47,48], in which the upstream and downstream information required in a standard LCA is reduced by using proxy data, qualitative models, and/or regression equations [49].

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FIGURE 17.7 Problem statement of the environmental assessment of chemical case study.

In a recent work [50], Calvo-Serrano and co-workers developed an approach that predicts the cradle-to-gate life cycle impact of organic chemicals from their molecular structure and thermodynamic properties. In essence, our method based on a mixed-integer programming (MIP) systematically constructs shortcut predictive models of life cycle impact using chemical attributes that lead to better predictions and low risk of overfitting. The MIP models contain binary variables denoting whether a given attribute is included or not in the regression model and continuous ones representing regression coefficients (Fig. 17.7). This modeling approach, which could be used to predict other properties, was applied to estimate the impact embodied in 83 chemicals from predictors chosen among 17 molecular descriptors and 15 thermodynamic properties. The impacts considered included the cumulative energy demand (CED), GWP, chemical oxygen demand (COD), biological oxygen demand (BOD5), total organic carbon (TOC), as well as Eco-Indicator 99 (EI99 Total) and the three impacts it aggregates, namely Human Health (EI99 HH), Ecosystem Quality (EI99 EQ), and Resources (EI99 Res). The results showed that adding more attributes in the predictive models (thermodynamic in addition to structural properties) leads to lower errors and, therefore, better predictions. The impact categories CED, GWP, EI99 Total, EI99 HH, EI99 EQ, and EI99 Res were predicted successfully with relative errors ranging from 20% to 44%, while COD, BOD5, and TOC were much harder to predict and showed much larger errors (see Fig. 17.8). Overall, this approach provides good estimates of impact without having to carry out tedious data collection tasks, thereby simplifying the environmental assessment of chemicals.

4. CONCLUSIONS In this chapter, we discussed the combined use of LCA and optimization, illustrating how it can be applied in the chemical industry. This method combines LCA, an effective tool to assess the environmental impact of a product or service over its life cycle, with MOO, employed to generate and screen alternatives considering economic and environmental aspects simultaneously. Three examples were presented, addressing problems at various scales from supply chain design to the environmental assessment of chemicals. As we approach planetary boundaries, the need to operate safely under tighter environmental regulations will become more critical, making these tools essential in the transition toward a more sustainable chemical industry. Future efforts should focus on developing methods for incorporating the main uncertainties affecting the calculations, algorithms for solving the underlying mathematical models more efficiently, and assessment methods linking inventory flows related to products and processes to global ecological limits (i.e., planetary boundaries).

REFERENCES

FIGURE 17.8

447

Absolute relative errors and predicted values for thermodynamic properties (TP), molecular descriptors (MD), and both combined (MDþTP) [50].

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