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Process Systems Engineering

A Computer-Aided Methodology for Mixture-Blend Design. Applications to Tailor-Made Design of Surrogate Fuels Lei Zhang, Sawitree Kalakul, Linlin Liu, Nimir O. Elbashir, Jian Du, and Rafiqul Gani Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b00775 • Publication Date (Web): 03 May 2018 Downloaded from http://pubs.acs.org on May 5, 2018

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Industrial & Engineering Chemistry Research

A Computer-Aided Methodology for Mixture-Blend Design. Applications to Tailor-Made Design of Surrogate Fuels Lei Zhanga*, Sawitree Kalakulb, Linlin Liua, Nimir O. Elbashirc, Jian Dua, Rafiqul Ganid a

Institute of Process Systems Engineering, School of Chemical Engineering, Dalian University of

Technology, Dalian 116024, China b

Department of Chemical Engineering, Auburn University, Auburn, Alabama 36849, United

States c

TEES Gas & Fuels Research Center, Texas A&M University at Qatar, 23874 Doha, Qatar

d

PSE for SPEED, Skyttemosen 6, DK-3450 Allerod, Denmark

KEYWORDS: Computer-aided mixture-blend design; Tailor-made surrogate fuels; Gasoline blend; Jet-fuel blend; Product property

ABSTRACT

Modern society needs various chemical products for its survival. The chemical products are classified in terms of single species products, multiple species products and devices. Multiple species products such as mixtures and blends are one of the most widely used chemical products. However, the common design methods for this kind of product are still mostly by trial and error

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or by rule based approaches. A computer-aided methodology integrated with experimental verification is presented in this article. In the first step of this methodology, model-based computer aided techniques are employed to the design of mixtures and blends. In the second step, the properties of the most promising product candidates are verified through experiments and/or rigorous models. The starting point is to analyze the product needs and translate them into target property constraints. A list of molecules that serve as ingredient-chemicals for addition to the blended product together with their pure compound properties are generated using a wellknown computer-aided design molecular design technique. A Mixed Integer Non-Linear Programming (MINLP) model is established for the selection of the ingredient-chemicals and their compositions in the blended product. The solution methods for the MINLP model are presented. For the first time, phase equilibrium based properties (such as liquid solution activity coefficients) are modelled and solved simultaneously in the MINLP model through the use of UNIFAC model. Finally, the optimization results are verified through experiments and rigorous models. Two application examples highlighting tailor-made surrogate fuel designs of a gasoline blend and a jet-fuel blend are presented.


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1. Introduction Chemical based products are made from a collection of chemicals, which meet specific needs of product functionality and utility. Nowadays, more than 70,000 chemical products are used in the modern society for its survival [1]. As Figure 1 shows, these widely used chemical based products include fuels for transport and energy; materials for the manufacture of cars, planes, housing and electronics; medicines for healthcare; foods for nutrition; fertilizers for agriculture; plastics for storage; paints for printing; and detergents for cleaning; etc. [2]. The chemical based products can be classified as single species products, multiple-species products and devices [2]. Single species products may be small molecules, such as solvents and refrigerants, or large molecules that serve as active ingredients in formulated and functional products, such as drugs, pesticides, detergents, cosmetics, lubricants and catalysts. Multiple species products contain more than one chemical, as in mixtures and blends, which are known as formulated and functional products. Blended products are commonly associated with a base product (single species or mixture) to which additives are added to enhance the qualities of the base product. Examples include tailor-made fuels (gasoline, diesel and jet-fuel with additives) and lubricants. Chemical devices are those chemical based products that perform a specific task-function, especially those with mechanical and electrical parts. Often, chemicals of a feed stream to a chemical device is transformed into an outlet stream with characteristics specified in the product attributes by performing reactions, fluid flow, heating/cooling, and/or separations [3]. For example, an indoor air purifier transforms an air stream laden with volatile organic compounds (VOCs) into clean air by catalytically decomposing the VOCs with platinum-doped TiO2 under UV irradiation. These special functions are performed through two types of devices: those that include mechanical parts and those that include also electrical parts. For example, fuel cells, and


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smart windows include electrical parts, while, inhalers for drug delivery and microcapsules for controlled release of pesticides do not include electrical parts.

Figure 1. Chemical products used in the modern society (adopted from Zhang et al. [2])

Design of chemical based products determines the molecular structure of a single species product or the identity of ingredient-chemicals and their compositions in multiple species products such that a set of desired properties and functions are satisfied. With a systematic design method, the optimal product can be determined from a large number of choices. Efforts have been made during last two decades to develop systematic methods, tools, software and databases for product design and development. Gani [3] reviewed computer-aided molecular design (CAMD) methods for product-process design. Grossmann [4] introduced product-process design as one of the future challenges of chemical engineering. Hill [5] proposed chemical product design as a third paradigm in chemical engineering. Ng et al. [6] reviewed significant developments, current challenges, and future opportunities using CAMD tools for chemical


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product design. Gani and Ng [7] reviewed product design focusing on product conceptualization. Zhang et al. [2] reviewed methods for chemical product and process design, discussed the challenges and opportunities, and the perspectives for the future of product process design. Fung et al. [8] proposed a grand chemical product design model, which consists of a process model, a property model, a quality model, a cost model, a pricing model, an economic model as well as factors such as company strategy, government policies and regulations. For different types of products, the commonly employed design approaches are also different. These solution approaches can be classified into experiment based, database search, heuristicrule based, model based computer-aided, and, integrated model based computer aided techniques combined with experiments. Experimental approach is often used for the design of big molecules, formulated products, functional products, and chemical devices. Experiment based design approaches are necessary in product design when many of the needed data are missing, and property model or related models are not completely understood. For example, the design of a conductive ink formulation [9] and the design of organic coatings [10] need to employ an experiment based technique because the necessary property models are not yet available. Database search is also an important tool for the design of products to reduce the search space. But it is applicable only for some design problems, usually for simple molecular product design problems. Heuristic-rule based approach uses a set of rules from a combination of experience, insight and available knowledge, where, the design problem is solved indirectly by first generating alternatives and then testing them for feasibility and selection. Application examples are the design of medicinal products (tablets and capsules) [11], and the design of cosmetics (creams and pastes [12]). Although heuristics-rules and experimental approaches often lead to a safe and reliable product, it is not practically feasible to evaluate all alternatives with these


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approaches, and they are valid within only a narrow product context. That is, a better product may have been missed [2]. Computer-aided approach is a promising technique for chemical product design. In such approaches, the product design problems are formulated as an MILP/MINLP optimization model, in which molecular structure, product property prediction model using group contribution method and mixing rules, and process model equations are the constraints. From the solution of the model, the optimal product is obtained. This approach was first applied to the design of single species products, and gradually expended to the design of multiple species products and other complex chemical products recently [13]. Gani and Brignole [14] proposed a synthesis-design method, called “generate and test”, to generate all feasible molecules from combining the groups, then estimate properties using group contribution methods for each generated molecule. The molecules within the range defined by the property constraints are kept as candidates. Conte et al. [15] developed a systematic computer-aided methodology for the design and verification of liquid formulated products. Yunus et al. [16] developed a systematic methodology for the design of tailor-made blended products and decomposition based approach is employed. Cignitti et al. [17] presented a framework for computer-aided design of pure and mixed chemical based products. In their framework, the product needs and target properties are systematically converted into an MINLP problem, and sequentially solved through a decomposed optimization approach. Zhang et al. [13] proposed a generic MILP/MINLP mathematical programming formulation, with the consideration of higher order groups for molecular structure representation and property estimation. Among all the chemicals based products, mixtures and blends are some of the most widely used products, and fuels such as gasoline continue to play a significant role in meeting the


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energy demand. Mixtures and blends contain two or more compounds (ingredient chemicals), usually as a liquid formulation or an emulsion. Surrogate fuels are special cases of blended products – they are formulated liquid solutions containing at least 4-5 compounds. They serve to represent the actual fuel, which contains 100s of compounds in terms of a selected set of properties. Surrogate fuels may be generated by studying a pallet of compounds to find sets of compositions that best match the target properties. Mueller et al. [18], and Kim et al. [19] used trial and error techniques combined with experiments and calculation models to identify the best surrogate. While experimentally validated surrogates could be obtained, these techniques are constrained by their initial set of pallet compounds. Also, experiments can be time consuming and expensive. Therefore, they are restricted to only a small set of surrogates. Ahmed et al. [20] employed an optimization based technique to find the optimal blend composition for a fixed number of pallet compounds. They included only a small set of properties that could be modelled. Therefore, computer-aided molecular design method is needed for the generation and quickly evaluate of all feasible pallet compounds. Yunus et al. [21] did not fix the number of pallet compounds and generated fuel blends with any number of compounds by applying modelbased optimization-based techniques. That is, Yunus et al. [21] for the first time added the identity and number of compounds in the blend also as a design variable. They, however, solved the resulting MINLP model through a decomposition based strategy where the original MINLP model is decomposed into a set of sub-problems. The main difference between the experiment based approaches and the model-based approaches is the list of properties considered. Obviously, the model based approaches can only include properties that can be modeled so that the included property can be reliably estimated. Therefore, some of the combustion or engine related fuel functions are not included in the model-based approaches.


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Improved experimental efforts to verify the performance of the surrogate fuels have been reported by many researchers. Mueller et al. [18] reported engine testing and chemical kinetics modelling as a function of composition and properties of surrogate fuels. Other works have employed compositional characterization of commercial fuels [22]; models for the thermodynamic and transport properties of the compounds comprising commercial fuels [23]; chemical-kinetic oxidation models for mixtures of compounds in commercial fuels [24]; computational models for physical/thermodynamic processes occurring within the engine [25]; numerical algorithms and computational hardware that can complete the required calculations in an acceptable amount of time [26]; as well as experimental capabilities to verify the propertiesfunctions listed above [27]. Although significant progress has been made in the testing of surrogate fuels, most start with a pallet of compounds from which they generate and test the blends. In some cases, the pallet contains for example, 9 compounds and an arbitrary number of blends consisting of 4-9 compounds are generated and tested. In others, only mixtures of the N pallet compounds are generated by altering their compositions. Yunus et al. [21], however, added the concept of main ingredient (MI) and additives to determine an optimal blend for a given set of target property constraints. Although, the correct model based optimization problem was formulated, their decomposition based solution approach was not computationally efficient because of the large number of potential candidate blends. Recently, Choudhury et al. [22] and [23] combined an extended version of Yunus et al. [21] with experimental verification of surrogate gasoline blends and jet-fuel blends, respectively. In this paper, a general model based optimization technique is presented for the design of tailor made surrogate fuels, in which the computer-aided molecular design method is applied to


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generate first all feasible compounds, then select a subset to determine which of them could be used in tailor-made surrogate fuels. First, a basic set of N (20-100 or more) compounds, selected from a database or generated through computer aided molecular design, is created. Then, a pallet of compounds or a MI is selected or generated from the N compounds. The design of tailor made blends is made by adding additives from the N compounds to the MI (representing also the pallet) such that a set of target property constraints are satisfied. Finally, the designs of the MI as well as the surrogate fuel (MI plus additives) are determined by formulating and solving the resulting MINLP models. For the first time, phase equilibrium based properties are calculated through the use of group contribution models like UNIFAC, which calculates the liquid solution activity coefficients for the compounds present in the mixture. The set of property constraints included are those for which validated models already exist. The best surrogate fuel candidates are further tested through a combination of experiments and rigorous modelling to validate the design. Here, other properties not included in the optimization step are also tested. In this way, a number of promising candidates are generated very quickly through model based computer aided techniques and focused experiments are performed only on the best candidates.

2. Computer-Aided Mixture-Blend Design Methodology Information-knowledge, methods, and data from different disciplines are needed for the design of mixtures and blends. A systematic computer-aided methodology is able to assist in the mathematical formulation and numerical solution of such design problems. The application range of the methodology depends on the model-data available for the desired (target) properties. The proposed methodology includes guidelines for the modeling and solution of the mixture-blend design problems with input/output information, calculation steps (including work-flow and data-


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flow), and associated methods and tools. The general work-flow of the methodology is shown in Figure 2. Brief explanations are given below for each step of the methodology.

Figure 2. Computer-aided design methodology for mixtures and blends Step 1. Identify product attributes The product attributes are identified in this step from interviews, surveys, knowledge and experiences from consumers and engineers. Different products have their specific product attributes, which should include functional requirements, engineering requirements, sensorial requirements and regulatory requirements. The functional requirements are the most important product attributes in this classification. It is the main reason for the consumer to buy the product.


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For example, a surrogate fuel needs to be burnt and run the engine efficiently. The engineering requirements are important and are typically required by product engineers, which include requirements such as flammability limit and product phase stability. The sensorial requirements include color, smell and taste, etc. Almost all products have such requirements. The regulatory requirements include safety, health and environment regulations, such as low toxicity and environmental friendly. Step 2. Convert product attributes to property constraints In this step, the product attributes are converted to physical and chemical properties, and their upper and lower bounds. Knowledge and heuristics-rules are commonly used in this conversion, and these properties can be obtained through databases, predictive models, or experiments. For example, the product form of homogeneous mixtures depends on the value of melting and boiling points of the product. These properties are determined through databases, group contribution methods, or measured experimentally. Here, the market study methods can be used to identify the product attributes and convert these qualitative product attributes into quantitative technical parameters (property constraints), such as Conjoint Analysis [30], Quality Function Deployment (QFD) [31] and Lead User [32] methods. However, some product attributes such as “do not oxidize to form unwanted by-products” for surrogate fuels, taste of food, odor of perfume, bioactivity and recyclable, which cannot be converted to any physical and chemical properties, only experience, heuristics and experiments can be used for the ingredient-chemical selection of these products. In this article, group contribution methods are applied for prediction of the required properties. A lot of researchers have developed various group contribution methods for different properties. As reviewed in [2, 3] and [7], more than 60 physical-chemical properties can be predicted using group contribution methods. Full list of these properties are


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summarized in Table S1 in the Supporting Information. Some of the predictive properties can be found in Figure 3. In Figure 3, the primary properties are the ones that can be predicted directly using group contribution method; the secondary properties are functions of primary properties; and the functional properties are functions of temperature (T) and pressure (P). All these properties can be integrated into the blending model for different requirements. Therefore, the proposed model can be used for various surrogate fuel design cases.

Figure 3. Partial list of predictive properties using group contribution method Step 3. Identify product ingredient-chemicals The mixture-blend type of products is obtained by mixing several selected compounds (ingredient-chemicals) together to obtain the desired product attributes. The ingredients can be classified as main ingredients (MI) and additives (ingredient-chemicals). For the design of


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surrogate fuels, a pallet of compounds (for example, hydrocarbon molecules) are needed to formulate the main ingredient, which is a mixture or blend that is a starting estimate of the optimal blend. Then the surrogate fuel (optimal blend) is obtained by mixing MI with additives (ingredient-chemicals) to obtain the final design. The ingredient-chemicals are used to find the initial MI as well as the final surrogate fuel. Step 4. Generate a basic set of ingredient-chemicals In this step, the single molecule ingredient-chemicals are first generated for each type of molecules (such as paraffins, naphthenes, aromatics, alcohols, etc.), which will be used for mixture-blend design step. The set of feasible blended product candidates are generated by mixing different ingredient-chemicals. Databases, heuristics and computer-aided methods can be used for the generation of single molecule candidates (ingredient-chemicals). For the generation of ingredient-chemicals, the same properties obtained from step 2 are used to restrict the number of candidate ingredient-chemicals. The database and heuristics are first used to generate an initial list of ingredient-chemicals within the pre-defined property constraints. Then computer-aided methods are used to extend the list with compounds that are not found in the databases used. A brief overview of the computer-aided method [13] used in this article is explained below. Functional groups are selected first as the building blocks for generating molecules. Since the fuel ingredients mainly include alkanes, alcohols, esters, ethers, ketones, acids, furans, etc., the functional groups that can form these molecular structures are only selected. Through the classification of groups based on their free attachments, the octet rule provides the relation for feasible molecular structures of a collection of groups. Eqs (1) and (2) shows the octet rule [33].

2 − = 2

(1)


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≥ − 2 + 2 ∀

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(2)

Here, is the number of group in the molecule, is the number of free attachments of

group , is assigned the value of 1, 0 or -1 for acyclic, monocyclic or bicyclic groups, respectively. In this paper, we only generate acyclic and monocyclic molecules. Therefore,

equals to 1 or 0. Within the set of group , there are several groups defines benzene ring structure, which is often appeared in the fuel ingredients. Therefore, the following equation is applied.

0 6 when = 0 = 0 when = 1

∈

(3)

In Eq. (3), !" is a subset of , which is the set of benzene ring groups. If the summation of the

benzene ring groups equals to 0, there is no benzene ring in the molecular structure, and could

be 0 or 1; when the summation equals to 6, there is benzene ring structure, and equals to 0.

Other structure constraints include the lower (# ) and upper ($ ) bounds of group (Eq. (4))

and the lower (%& ) and upper (%'( ) bounds of total group number (Eq (5)).

# ≤ ≤ $

∀

%& ≤ ≤ %'(

(4) (5)

There are also property constraints, which restrict the property ranges of the candidate

ingredient-chemicals. In Eq. (6), * is the set of all the target properties of the molecule, and their

property range should be within the bounds +,-# , ,-$ /. These property bounds are decided from

knowledge and heuristics, which is adjusted to screen out some ingredient-chemicals from a mixture-blend product.


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,-# ≤ ,- ≤ ,-$

∀0 ∈ *

(6)

From the solution of above Eqs. (1)—(6), a list of candidate ingredient-chemicals that can serve as candidate for addition to MI (or pallet) as well as the final mixture-blend. Step 5. MILP/MINLP model formulation After the generation of the dataset of ingredient-chemicals, mixture-blend design problems are formulated as MILP/MINLP models, represented by Eq. (7). The objective here is to find the product formula with optimal product quality ( ) or economic indexes (1). The related property model (23%, ), process model (24 ), pricing model (23%54 ), economic model (26 ) as well as other issues such as social and environmental impact are the constraints of the MINLP model.

min/max 1 or

(7)

subject to:

>,- = ? -

∀

, = 23%, @A , ,B, C ,# ≤ , ≤ ,$

∀D

(Ingredient-chemical selection)

∀D

(Property bounds)

B = 24 A , ,E

(Process model)

= 2F @, C

(Quality model)

*3%54 = 23%54 1 = 26 @*3%54 , BC

(Mixture property model)

(Pricing model) (Economic model)

0 ≤ A ≤ 1, >,- ∈ G0,1H

In Eq. (7), is the set of ingredient-chemicals involved in the product, D is the set of properties

need to be considered in the design problem, 0 is the set of candidate ingredient-chemicals for each ingredient. In the objective function, 1 is a vector of economic indices, such as NPV (Net ACS Paragon Plus Environment

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Present Value) and IRR (Internal Rate of Return); is the product quality (a function of product

properties). For each ingredient-chemical , binary variable >,- indicates if a certain candidate 0

is selected for . And for , only a certain number of the candidate ingredient-chemicals (? ) is

selected. 23%, is the mixture property model for property , . The set of properties are obtained

from step 2. , could be a linear or non-linear function of composition A and pure compound

property ,B, derived from thermodynamics or from the regression of experiments. The pure

compound properties are obtained from the database or predicted from group contribution methods as discussed in step 4. For the design of mixture-blends, the mixture property model is important as it calculates the product properties directly from the ingredient-chemical compositions. Typically, for the design of surrogate fuels, mixture-properties include vaporliquid phase behavior (for example, Reid vapor pressure and flash point) for which the activity coefficients of the compounds present in the liquid solution are needed. The UNIFAC group contribution based model for liquid activity coefficients is a predictive model and therefore selected for use in the methodology. Addition of the UNIFAC model equations and the resulting phase equilibrium calculations makes the optimization model highly non-linear resulting in a non-convex objective function. For each property , , its upper (,$ ) and lower (,# ) bound are

set. These properties and bounds are obtained from step 2. 24 is the process model which is a

function of composition A and processing parameters ,E. It determines the production cost B. 2F

is the quality model, which is a function of the product properties. For example, in the fuels or lubricants design problem, viscosity is a very important property, and it is selected as the quality factor. 23%54 is a pricing model. The price of the product can be determined of its quality. 26 is

the economic model, in which the economic indexes, such as NPV and IRR. It is determined by the product price *3%54 and the product cost B . In this article, the process model and the ACS Paragon Plus Environment

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economic model are not discussed in detail, only models related to product ingredients selection and composition are discussed. Step 6. Solve the optimization model The established MILP/MINLP model is solved in this step. Optimization software such as GAMS (www.gams.com), MATLAB (www.mathworks.com), LINGO (www.lindo.com) etc. can be used to solve the model directly. However, due to non-linear property models, a large number of mixture-blend design problems are difficult to solve directly. Karunanithi et al. [34] and Yunus et al. [21] proposed a decomposition-based algorithm to solve the MINLP problem by first solving sub-problems consisting of a set of property constraints only. The search space is then reduced by using the decomposition algorithm by converting the original MINLP problem to several Non-Linear Programming (NLP) problems corresponding to fixed values of the integer variables. Other algorithms can also be applied based on different problems. As discussed in step 5, the addition of the UNIFAC equations and phase equilibrium conditions make the mathematical problem highly non-linear. Thus, it is difficult and solution method is not robust enough to use optimization solvers to solve these problems directly. Therefore, a two-steps solution strategy is used to solve the MINLP model, which is highlighted in Figure 4.


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Figure 4. A two-steps solution strategy for mixture-blend design problems The optimization model (Eq. (7)) can be reformulated as the following equations (Eq. (8)):

min/max 1 or

subject to:

(8)

IA, >, J ≤ 0

J = K?LMNOA, >

In this formulation, is the optimal product quality. For example, for the surrogate fuels design problem, the main ingredients consumption which are obtained from crude oil need to be minimized, therefore the min ingredients composition is the product quality. A is the ingredient

composition, and the binary variables > indicate if a candidate ingredient-chemical is selected as

an ingredient-chemical, and J is the activity coefficients. Here, A, > and J are all vectors which include all compounds of the product. The constraints of the optimization model are groups into

two parts: Constraints I denote the constraints for ingredient-chemical selection, mixture property model equation and property bounds (except the UNIFAC equations) which are


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presented in Eq. (7). The mixture properties include density, Reid vapor pressure, viscosity, etc.

The constraint K?LMNO represents the UNIFAC equations (see Eqs. (24)—(35) in the gasoline

design case study), which are highly non-linear. First, the activity coefficients JP are fixed to a

constant (for example one), and in step 1, the optimization model is solved without the UNIFAC

equations, the solutions are (AQ , >Q ). Then, in step 2, JQRS is obtained by inserting (AQ , >Q ) to the UNIFAC

equations.

Similarly,

we

could

calculate

a

list

of

activity

coefficients:JP , JS , JT , … , JQ , JQRS . When |JQRS − JQ | ≤ W W = 11 − 6, the iteration stops, and

the final solution is determined by fixing the activity coefficients to JQ . This approach can be applied for all mixture-blend design problems when most of the nonlinear equations appear in the equality constraints, such as NRTL, UNIQUAC, etc. Note that this step maybe applied to generate the starting MI (or pallet) surrogate fuel as well as the blend of MI plus ingredient-chemicals (additives) to match the target properties. Step 7. Verification and experimental iteration In previous 1-6 steps, the product ingredient-chemicals and their compositions are obtained from the solution of the optimization model. The mixture-blended product is then synthesized in the laboratory. In this step, the physical and chemical properties are verified through experiments and/or other rigorous models, such as ICAS [35] and VPPD-LAB [36]. Then, the experimental iteration is performed guided by a causal table until the desired product attributes are achieved.

3. Application Examples Nowadays, fossil fuels continue to play a significant role in meeting the increase in energy demand. Alternatively, surrogate fuels are developed with their ingredients and additives from renewable sources. A surrogate fuel is one that comprises of a small and diverse number of


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compounds that mimic certain target characteristics of the original fuel [37]. Currently, many products are derived from fossil fuel based raw materials and from a sustainability point of view, renewable alternatives need to be considered, whenever feasible. In order to achieve this, tailormade surrogate fuels need to be developed by blending the conventional materials with other chemicals that can be produced from renewable resources, namely, bio-based chemicals. The advantage is the amount of fossil fuel consumption is reduced. At the same time, the chemical based products are safer for humans and environment because the harmful chemicals are removed or replaced with safer chemicals from the product design method. In addition, the product attributes can be improved by adding chemicals (additives) that have potential to enhance the specific product attribute [38]. Here, two application examples are presented to illustrate the mixture-blend design methodology for tailor-made design of surrogate fuels: gasoline blend and jet-fuel blend. The designed surrogate fuels are used for (i) simulation of the original fuels by using a smaller number of molecules (MI); (ii) study which additives should be added and their composition to formulate the surrogates with the desired properties; (iii) obtain the composition of the tailor-made surrogate fuels based on customers’ diverse requirements.

3.1 Gasoline blend design Blending gasoline with additives helps to improve its performances and reduce the consumption of conventional gasoline. In this case study, the blend design of gasoline is formulated as an MINLP problem. The conventional gasoline is blended with candidate ingredient-chemicals derived from renewable sources, called bio-based chemicals. The bio-based chemicals available in the gasoline database are alcohols with low carbon number (C2—C5), ethers, ketones, acids and furan derivatives. The blends may consist of two or more ingredient-


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chemicals (in addition to the MI or pallet chemicals) to form either binary or ternary mixtures with the MI. The selection of feasible blend candidates as well as their composition are determined using the proposed computer-aided design model, and the infeasible ingredientchemicals are screened out as infeasible because of the given specifications. Step 1. Identify product attributes From market study, the gasoline blend should have the following product attributes [38]: it can be burnt and run the engine efficiently; it can flow continuously from the fuel tank to the combustion chamber; it should have a suitable flammability limit; and have low toxicity as the requirements of environment health & safety. In addition, the gasoline blends must be stable liquid solutions, which mean that the blends do not split into two liquid phases, do not oxidize to form unwanted by-products, and must not evaporate easily. Note that a blend is desired because a single molecule cannot match a diverse set of properties. Step 2. Convert product attributes to property constraints The product attributes are converted into properties using knowledge and experience. These knowledge and experience are from thermodynamics, physics and chemical engineering. There are indeed many ways to translate these product attributes to properties and although different surrogate fuels require different property sets, there is a sub-set that are usually common to all, but having different property bounds. Therefore, for tailor-made fuel design, an already developed database is used to retrieve the properties corresponding to the specified product attributes. Table 1 lists the target properties and their upper and lower bounds retrieved from the database. Table 1. The product needs and target properties of gasoline and jet-fuel design problem Example

Gasoline blend

Jet-fuel blend


21


Product Attributes Ability burned

to

Flammability

Target property be Reid vapor pressure (kPa) Flash point (K)

Engine efficiency Higher heating value (MJ/Kg) Density at 15 oC (Kg/m3) Consistency fuel flow Phase stability

Environmental impacts

of Dynamic viscosity at 20o C (cP) Gibbs energy of mixing

Oxygen content (wt%) Lethal concentration, 50% (mol/L)

Property constraint

45 ≤ Z[* ≤ 60 2\ ≤ 300.15 __[ ≥ 35

720 ≤ b ≤ 775 0.3 ≤ c ≤ 0.6 efgA < 0

E Δf %( i k

A Computer-Aided Methodology for Mixture-Blend ... - ACS Publications

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