Chemical product design – recent advances and perspectives

Chemical product design – recent advances and perspectives

Available online at www.sciencedirect.com ScienceDirect Chemical product design – recent advances and perspectives Lei Zhang1, Haitao Mao1, Qilei Liu...

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ScienceDirect Chemical product design – recent advances and perspectives Lei Zhang1, Haitao Mao1, Qilei Liu1 and Rafiqul Gani2,3 Chemical industry is continuously looking for opportunities to manufacture the necessary commodity chemicals as well as to convert them into higher value-added chemicals-based products. Development of these chemicals-based products involves not only their design and/or selection but also their sustainable manufacturing through an appropriate chemical process, its marketing and its disposal as waste. This perspective paper considers mainly computer-aided methods and tools suitable for chemicals-based product development. The advantage of computer-aided design methods and tools is that it is possible to quickly identify promising candidates that can be further investigated and verified through focused experimentbased techniques to obtain the final optimal design. The disadvantage of using these models and/or data-based approaches is that their application ranges are limited to the available models, data and knowledge related to the currently known products. Another complexity that needs to be considered is the multiscale and multidisciplinary nature of chemicals-based product design problems. Therefore, to find new and innovative chemicals-based products, systematic computer-aided methods and tools, capable of managing this complexity are needed. In this paper, the frontiers of model and/or data-based methods for systematic chemical product design and application are presented. Various computer-aided design methods and tools including experiment-based, knowledge-based, rule-based and model-based approaches are briefly reviewed. Perspectives including challenges and opportunities in computer-aided chemicals-based product design are discussed. Addresses 1 Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China 2 PSE for SPEED, Skyttemosen 6, DK-3450 Allerød, Denmark 3 State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China Corresponding authors: Zhang, Lei ([email protected]), Gani, Rafiqul ([email protected])

Current Opinion in Chemical Engineering 2019, 27:22–34 This review comes from a themed issue on Frontiers of chemical engineering Edited by Rafiqul Gani

https://doi.org/10.1016/j.coche.2019.10.005 2211-3398/ã 2019 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Current Opinion in Chemical Engineering 2020, 27:22–34

Introduction Current global business environment encourages a short time-to-market for any potential chemical product (through this paper, ‘chemical product’ will mean ‘chemicals-based product’). Therefore, the focus has been shifting from process design for business-to-business (B2B) chemicals to product and process design for business-to-consumer (B2C) products [1]. In chemical product design, we try to find a single chemical or a chemicals-based mixture or blend that exhibits or enhances certain desirable and/or specified functional properties of the product. Nowadays, chemical product design is being regarded as a new paradigm in chemical engineering [2]. During last three decades, efforts have been made to develop databases, design methods and associated software tools for chemical products. However, because of the multiscale and multidisciplinary nature of many chemical product design problems, various other disciplines such as computational chemistry, thermodynamics, material science, chemical engineering, industrial engineering, electronic engineering, data science and artificial intelligence also need to be incorporated within the design methods and associated software tools. Traditionally, chemical products are designed and developed through heuristic rule-based and/or trialand-error experiment-based approaches. Although these kind of approaches often lead to safe and reliable product designs, it is not practically feasible to evaluate all alternatives [3] or to obtain the optimal solution. Recently, the use of model-based design methods has been gaining increased attention as they have the potential to generate and/or screen feasible product candidates in a much larger design space, and at the same time, reduce the time and costs for their development. If necessary data are available and the models giving reliable estimations for product properties and functions are available, it is possible to develop and use model-based chemical product design approaches with the advances in computer-aided technologies. A framework for chemical product design is shown in Figure 1. For a given product design problem, the product needs are first identified and then translated to a set of corresponding target properties. Next, an analysis of the available data, models, need for experiments is made. According to the analysis, a mathematical representation of the synthesis-design problem is established, consisting of an objective function (related to cost and/or product performance criteria) and constraints www.sciencedirect.com

Chemical product design – recent advances and perspectives Zhang et al. 23

Figure 1

Chemical products

Mathematical models



Objective functions (Min/Max properties/Process variables/Economic indicators)

Algorithms

s.t

Identify product needs

5) process models

1) Model parameter constraints;

2) Economic models;

3) Property models;

4) Product performance models;

Translate needs to properties

Structure-property relationship

Transmission and reaction process

Solution approaches

Process-related

•Database •Experiments •Heuristics •Theoretical models •Data-driven models

Analyze available data, models, etc.

Economic analysis

Prototyping

Product launch

Time-scale

Product design model Physico-chemical property-related Modeling at mesoscale Mixing rules (Nano-objects) Group (Molecules/ contribution Mixtures) methods Molecular (Groups) Dynamics Quantum (Atoms) Mechanics (Electrons)

Process unit PBM and CFD simulation Phase simulation (Process unit) equilibrium (Mass/energy (Thermotransfer) dynamics)

Process simulation (Process)

Multiscale chemistry/chemical engineering mechanism study Length-scale Current Opinion in Chemical Engineering

Framework for chemical product design (Figure adopted from Uhlemann et al. [4] with permission from Wiley).

representing the translated needs to target properties, the process model equations as well as product-process structural conditions. Note that the constraints are represented by a set of models: property models, process models, economic models, environmental impact models, models for sustainability indices and many more. Algorithms are needed to solve the mathematical problem, and from the solution, the optimal product candidates are obtained, which could be further analyzed for economic analysis, prototyping, and finally, launch to the market. To formulate a product design problem, relationships between product ingredients/structures and product properties need to be established first through a set of process-property models. These models should preferably be predictive and could be developed through a combination of techniques such as Quantum Mechanics (QM), Molecular Dynamics (MD), Group Contribution (GC), and Quantitative Structural Property Relationship (QSPR) for property models; and Finite Element (FE), Computational Fluid Dynamics (CFD), and steady state-dynamic lumped for process models, including product performance models. Also, machine learning based models coupled with available data could help to establish the needed process-property models if sufficient data-knowledge is not available. www.sciencedirect.com

Many review articles focusing on chemical product design problems have been published. In the text below, only those published after 2000 are considered. Zhang et al. [1,3] reviewed various aspects of integrated productprocess design. Recently, Uhlemann et al. [4] reviewed the past, present and future for product design and engineering. Gani [5] reviewed aspects of product and process design using Computer-Aided Molecular Design (CAMD) techniques, while Grossmann [6] introduced chemical product design as one of the future challenges of chemical engineering. Ng et al. [7] reviewed the development, challenges and future opportunities of chemical product design using CAMD tools. Gani and Ng [8] reviewed chemical product design focusing on product conceptualization. Austin et al. [9] reviewed the tools, applications and solution techniques for CAMD. Butler et al. [10] summarized recent progress on molecular and materials design using machine learning methods. Gani [11] reviewed the group contribution-based property estimation methods for product design. Ng and Gani [12] proposed directions for research and teaching for chemical product design. This perspective paper focuses on systematic computeraided methods and associated software tools for versatile Current Opinion in Chemical Engineering 2020, 27:22–34

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chemical product design and discusses important issues, challenges and some perspectives. The rest of the paper is organized as follows. In Section ‘Classification of Chemical Products’, the classification of chemical products with examples and commonly used design methods are given; in Section ‘Model-based methods and associated software tools for Chemical Product Design’ a collection of modelbased systematic computer-aided methods and associated software tools used in chemical product design are summarized; in Section ‘Perspectives’, the challenges and perspectives for chemical product design are discussed.

Classification of chemical products

Zhang et al. [3] classified chemical products as single species products, multiple species products and devices. Single species products are subdivided into small and large molecules, whereas multiple species products are subdivided into formulated and functional products. In this perspective paper, a selected set of product types [13], which could be single species and/or multiple species are highlighted in Table 1 with examples and associated references, the basis of their typical design methods and a list of likely design challenges. The basis for inclusion of a product type in Table 1 is their published application example and the use of model-based and/or data-based design methods in one or all steps of the design process. Obviously, as the number of chemical product types is numerous not all chemical product types are listed in Table 1. Some of the challenges in design listed in Table 1 are further discussed below. Here, only challenges A to E are discussed in detail (as they are more developed), the rest

(F to I) are also important and are discussed in Section ‘Perspectives’ under ‘perspectives’ (as they need to be further developed). (A) Structure-property relationship (Absence of engineering science and/or knowledge). While many organic chemicals-based products can be routinely designed through structure-property relationships, for some properties and more complex chemical formulations and materials, the theory is not well established and so, data-driven methods such as machine learning are finding increasing use to extract structure-property relationships from collected data. Structure-property relationships therefore need to play a very important role in chemical product design, for example, in design and/or selection of active pharmaceutical ingredients, membranes, and catalysts. The challenge, however, is to convert the available data and/or correlations into predictive models, for example, in emulsified chemicals-based products and/or other structured or functional products that require properties, such as Krafft temperature, hydrophiliclipophilic balance and interfacial tension. Note that while correlations exist for these properties, predictive models needed to design the emulsifier or detergent products are more difficult. Kontogeorgis et al. [29] have highlighted the design of emulsified products through a combination of predictive models, correlations and data-search, while Fung et al. [30] highlighted the design of a detergent product through correlations, data and experiments. The structure of the micro-emulsion, for example, in

Table 1 Product types with examples of application, basis of design methods and challenges in design Product type

Examples

Basis of typical design methods

Challenges in design a

Coatings and sensors Process fluids Fertilizers and pesticides Food

Nanoparticle-based materials [14] Solvents [15], refrigerants [16] Pesticides [17] Food flavor and sensory [18]

A, A, A, A,

Healthcare

Drug discovery [19]

Skin-protection Dyes and pigments

Insect repellent lotion [20] Polymethine dyes [21]

Fragrances

Perfumes [22], shampoo additives [23]

Special separations

Polymers for CO2 capture and separations [24] Surrogate fuels and blends [25] Heterogeneous catalysts [26]

Heuristics, experiments CAMD, database, heuristics, experiments Heuristics, experiments Regression, Artificial Neural Network (ANN), experiments Machine learning, database, heuristics, experiments Heuristics, database, CAMD, experiments Expert knowledge, experiments, heuristics, quantum mechanics Expert knowledge, database, experiments, machine learning Expert knowledge, experiments, molecular dynamics Heuristics, database, experiments, CAMD Expert knowledge, experiments, Ab initio calculation, machine learning Experiments, heuristics, expert knowledge, QM, MD, meso-scale modeling, CFD

Energy provider Reaction promotion Functional device

Rechargeable batteries [27], micropower-generation devices [28]

B, B, B, B,

C, C, C, C,

D, D, D, D,

G F, G, I E, F, G, H, I E, F, G, H, I

A, B, C, D, E, F, G, H, I A, B, C, D, E, F, G, H, I A, B, C, D, E, F, G, H, I A, B, C, D, E, F, G, H, I A, B, C, D, E, F, G, H, I A, B, C, E, F, G, I A, B, C, D, E, F, G, H, I A, B, C, D, F, G, H, I

a

A. Structure-property relationship (absence of engineering science and/or knowledge); B. Multiscale complexity; C. Safety, environmental impact and sustainability; D. Processing routes; E. Translation of needs to properties; F. Time to market; G. Feedstock availability; H. Scale-up issues; I. Coupling product design and process engineering.

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Chemical product design – recent advances and perspectives Zhang et al. 25

design of fat-free margarine, needs to be established through experiments. Similarly, for polymer-based products not all target properties, namely, elasticity, sheer stress, and so on, can be estimated through predictive models. Also, the property dependence related to arrangement of the polymer repeat units and/or the selection of co-polymers in chemical product design need more data so that model-based techniques may be used. A better understanding of the principles governing these product properties and functions are needed to develop the necessary models and thereby, advance the design of these types of products. Also, to increase the application ranges of currently available organic chemicals-based products, computational chemistry-based methods including QM, MD and even FE/CFD may be used if sufficient structural information and/or data are available to fill-in the gaps in properties that cannot be currently reliably measured. (B) Multiscale complexity. Multiscale approaches provide opportunities to consider necessary system description and associated details for simultaneous processproduct design. For example, CFD-based model of the crystallizer operation could be used together with the end-user (macro-scale) property models for design and/or selection of solvents (and/or anti-solvents). Another example is the design of functional materials, such as catalysts [26] or membranes [31], where quantum mechanics could be employed to design the product microstructure while a macroscale model is used for the process simulation and/or design. To establish a multiscale design model, modeling methods and tools from atom and molecular scales to equipment and process, and even enterprise-wide scales need to be considered together with connections between different scales [32]. To manage the complexity of multiscale models, model reduction and solution strategies, together with the derivation and use of surrogate models constitute a challenge worth considering. As in the case of property modelling, the application range depends on the available process models. Models for processes such as etching, coating, soap processing, vapor deposition, for example, are still not available. environmental impact and (C) Safety, sustainability. Mishandling of hazardous materials is one of the main factors that result in industrial catastrophe [33]. Although these are important safety-related design issues, the challenge of systematic design of chemical products considering safety, environmental impact as well as sustainability in the early stages of the design process still needs to be resolved. Here, the safety, environment and sustainability related product (performance) property models are needed. For example, LC50 and GWP (global warming potential) are safety and environment related properties; VOC (volatile organic chemicals) www.sciencedirect.com

is operational safety-hazards related issue, for example, as benzene is likely to vaporize and because of its carcinogenic properties, it is considered an unsafe and hazardous material, which needs to be substituted with a benign chemical having similar functions as benzene. Similarly, chemicals with low flash point or auto-ignition temperatures may cause explosion during operation of the process; therefore, they also need to be replaced. A major challenge here is the selection of appropriate EHS (environment, hazards and safety) indexes and the definition of their limiting values. In this respect, a general method for chemical substitution [34] is a step in the right direction. (D) Synthesis routes. Two aspects of synthesis route related to chemical product design are considered: 1) Reaction synthesis to identify the reaction path needed to convert specified raw materials (reactants) into the desired chemicals (products). For chemical products, especially large molecular products, an additional incentive (or challenge) is to determine an easier synthesis route that can make the production of a product candidate more sustainable and economically feasible [3]. Systematic guidelines for determining the optimal synthesis route for the conversion of a candidate product from specific raw materials are needed, and the recent uses of QM and machine learning based methods for synthesis routes identification-selection are highly encouraging [35]. 2) Processing routes for the production of a chemical product. Single species relatively small chemical products are also usually classified as commodity chemicals whose processing route selection design are well established, and, which are produced in large amounts for economic feasibility. Examples of these types of products are solvents and refrigerants. Blended products such as fuel blends also require relatively simple processing routes. However, processing routes for functional products such as soaps or detergents are still based on trial-and-error approach as appropriate propertyprocess models are still not available [2]. The processing routes needed for the manufacture of pharmaceutical products represent another challenge as they need to be developed rapidly as well as they need to be first time right [36]. Other unconventional processing techniques namely Physical Vapor Deposition (PVD)/Chemical Vapor Deposition (CVD), etching, printing, and so on, usually need to be considered for many functional (devices) products. Currently, these are determined through experiments with selected product candidates. That is, the product selection-design could be based on different design approaches, including model-based techniques, but the product validation is through experiment-based techniques. Another challenge is to find new, innovative and more sustainable Current Opinion in Chemical Engineering 2020, 27:22–34

List of selected commonly used methods and their associated computer-aided tools employed in chemical product design Methods

Basis

Computer-aided tools

Pros and cons

Experiments

Trial and error [38] Design of experiments [39]

– Minitab (www.minitab.com)

Pros: Theory and knowledge are not needed; leads to safe and reliable products; Cons: Limited test opportunities; costly; timeconsuming, no guarantee of solution optimality Pros: Properties are reliable; fast generation of feasible candidates list; Cons: Not all molecules are listed; limited availability of property data; potential solutions may be missed. Pros: Lead to safe and reliable products; quick and relatively inexpensive solution; easy to apply when rules are available; Cons: Better products may be missed; experience, insight and knowledge are needed; rules are sometimes contradictory; valid within a narrow context.

Databases Database search [40]

Heuristics

NIST Chemistry webbook (www.webbook.nist.gov/chemistry/) PubChem (www.pubchem.ncbi.nlm.nih.gov) ZINC (www.zinc.docking.org) DRUGBANK (www.drugbank.ca) ProCAPD (www.pseforspeed.com/procapd) KIFS (Knowledge-based formulation system) [43] ProCAPD (www.pseforspeed.com/procapd)

Heuristic rule-based design [41,42]

Model-based (direct) Group contribution [44]

Thermodynamics [45] All equations representing the mathematical problem (see Table 3) are solved directly Process simulation [46,47]

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Data-driven [19]

ICAS – ProCAMD (www.pseforspeed.com/icas/ procamd) ICAS – ProPred (www.pseforspeed.com/icas/ propred) OptCAMD [44] AMODEO (www.archimedes.cheme.cmu/edu/? q=AMODEO) DDBSP (www.ddbst.com/unifac-calculation) COSMOtherm (www.cosmologic.de/products/ cosmotherm) SCM – COSMO-RS (www.scm.com/product/ cosmo-rs) Aspen Plus (www.aspentech.com/products/ engineering/aspen-plus) PRO/II (www.sw.aveva.com/ engineer-procure-construct/ process-engineering-and-simulation/ pro-ii-process-engineering) gPROMS (www.psenterprise.com/products/ gproms) SPSS (www.ibm.com/analytics/ spss-statistics-software) Matlab (www.mathworks.com/products/matlab. html) Python: TensorFlow, Theano, Caffe, Scikit-learn, Keras

Pros: Global optimal solutions; no need to have in-depth knowledge once the model is ready; can be applied to various kinds of problems; Cons: Size and complexity of the model; a lot of physicochemical phenomena occurring are not completely understood; lack of relevant property models for some products; the error of the property model.

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Table 2

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Pros: Near optimal solutions; wide range of problems can be solved; Cons: Integration of tools for different subproblems can be difficult; solution strategy needs to find the calculation order for the subproblems. General mathematical problem is decomposed into a set of hierarchical subproblems, which are solved with their associated tools Model-based (hybrid)

The most complex subset of equations representing the mathematical problem are converted into simple local (surrogate) models and solved in the inner-loop (as direct method) and rigorous (complex) models are solved in the outer-loop or off-line to generate the surrogate models

Gaussian (www.gaussian.com) VASP (www.vasp.at) ORCA (www.orcaforum.kofo.mpg.de) QM [48] GAMESS (www.msg.chem.iastate.edu/gamess) Material Studio (www.3dsbiovia.com/products/ collaborative-science/biovia-materials-studio) Material Studio (www.3dsbiovia.com/products/ collaborative-science/biovia-materials-studio) LAMMPS (www.lammps.sandia.gov) MD [49] VASP (www.vasp.at) Gromacs (www.gromacs.org) Ansys Fluent (www.ansys.com/products/fluids/ ansys-fluent) CFD [50] COMSOL (www.comsol.com) OpenFOAM (www.openfoam.com) Template-based design of mixtures and blends [25,51] ProCAPD (www.pseforspeed.com/procapd)

Basis Model-based (indirect)

Methods

Table 2 (Continued )

Computer-aided tools

Pros and cons

Pros: Near optimal solutions; wide range of problems can be solved; Cons: A lot of physicochemical phenomena occurring are not completely understood; lack of relevant property models for some products; an extra-loop for the solution strategy.

Chemical product design – recent advances and perspectives Zhang et al. 27

processing routes for any established process, where multiscale techniques for process intensification [37] promise significant improvements. (E) Translation of needs to properties - The design of chemical products depends very much on the ability to represent the product characteristics by a set of measurable and/or predictable properties. Much work is needed a priori to obtain the data needed to translate product needs to target properties. For example, it is necessary to gather information from consumers in terms of basic needs (the main functions of the products) and additional needs (which, if they are fulfilled, would improve the product quality) as well as know-how to translate the consumer needs into target properties. Usually, an expert would be able to provide this knowledge if the necessary data are available. Another option is to collect necessary data from published expertinsights, open-source databases, patents, published articles, and so on, to prepare the translation of needs to properties tables in the same way that Conte et al. [20] did for liquid formulated products and Kalakul et al. [25] did for different blended fuels. It should be noted that this is a time-consuming work but availability of data in table avoids this extra work. For a new type of chemical product not covered by the established tables (for example, in ProCAPD [25]) in a product design software tool, extra work would be needed to establish the translation.

Model-based methods and associated software tools for chemical product design Chemical product design problems are multidisciplinary and multiscale in nature and therefore, they need different methods and software tools from different disciplines with associated different scales. These commonly used design methods and tools, as well as their pros and cons, are listed in Table 2. Experiments

Traditionally, chemical products are developed through experiment-based trial-and-error methods by specialists. For many chemical products with complex molecular structure or ingredients, experiments are necessary because when data and property models are missing, it is impossible to use model-base approaches for the design. The missing data or models have to be obtained through experiments. Although experimental approaches often lead to safe and reliable products, it is not practically feasible to evaluate all alternatives, which is costly and time-consuming, even when DoE (Design of Experiments) is adopted to accelerate the experiments. Therefore, better products may be missed using experimental approaches. Tam et al. [52] used experiments to design conductive inks, in which they generated the specific surface energy model of Polyethylene terephthalate (PET) substrate using experiments for the selection of Current Opinion in Chemical Engineering 2020, 27:22–34

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dispersing medium (for example tetradecane, ether, toluene and cyclohexane). Databases

When enough experimental results are available, a database of properties could be established. Database search is usually applied to the design of some simple molecular products and ingredients selection of some formulated products. Although databases are useful tools for preselection of product candidates, they do not provide the final design, even though they provide a fast and relatively cheap option for chemical product design. A complexity is the management of databases as different types of chemicals and/or materials require separate databases with their separate ontologies for knowledge representation. For examples, separate databases are needed for solvents, aroma compounds, active ingredients for different types of functional products, refrigerants, membranes, adsorbents, and many more. Since different product types have different sets of property requirements, their ontologies for knowledge representation are also different. For example, solvents are characterized with different sets of properties than membranes. Gu et al. [53] collected 197 201 natural product structures and reported their biological activities for the virtual screening step in drug discovery. In accordance with the established drug database, they successfully selected the possible natural product to treat a typical disease using data analysis tools such as principal component analysis. However, with their database, it is still impossible to screen drugs with acceptable properties including IC50, solubility, and so on, because these properties are missing in the database and they are also important in drug design. Similarly, Dionisio et al. [40] established a database for consumer products, in which more than 75 000 chemicals and more than 15 000 consumer products are included. The data’s primary intended use is for exposure, risk and safety assessments. Although many databases are available online, only some of the most common molecules and their physical parameters are, however, available, and the product design space is limited to the size of the database. Heuristics

Heuristic rules help to make design decisions, make appropriate selections, which avoids the use of mathematical programming-based design approaches. These rules are commonly derived from a combination of experience, insight and available knowledge. The major difficulty is that often the rules are contradictory and difficult to apply, and only valid within a narrow product context. Nevertheless, the heuristic rules are often simple, and potentially promising solutions can be obtained very rapidly and the potential search space significantly reduced for similar problems. Wibowo and Ng [54] used heuristics-rule-based methods for the design of cream and paste products. Current Opinion in Chemical Engineering 2020, 27:22–34

Model-based design approaches

In model-based design approaches, the product design problems are formulated mathematically. Considering the models and variables involved, the design problems could be formulated as LP (linear programming), NLP (non-linear programming), and MILP/MINLP (mixedinteger linear/non-linear programming). A generic problem formulation [3] is listed in Table 3. For process simulation (Eq. (3)), or property prediction (Eq. (4)) with only linear equations involved, there are no integer variables in the mathematical problem. Therefore, the design problem is simplified to an LP problem, for example, prediction of Hansen (dispersive) solubility parameter for a given molecule using a GC method [55]. If nonlinear terms are involved in Eqs. (3) or (4), the problem becomes an NLP problem, for example, prediction of normal melting point for a given molecule [55]. For molecular design problems with only linear property models (Eqs. (1), (4) and (6)), the design problem becomes an MILP problem, for example, a surfactant design problem using CAMD [56]. If the product design problem contains nonlinear terms, the model is a MINLP problem, for example, a solvent molecule design for liquid-liquid extraction [44]. Quantum mechanics (QM). QM methods are used to establish the missing product structure-property relationships by employing ab initio calculations. In the generic problem formulation (Table 3), when the property model (Eq. (4)) is missing, it is possible to use QM methods to obtain the property values. For example, computer-aided catalyst design using Density Functional Theory (DFT) [57]. Another example is the reaction solvent design problem, in which the reaction rate prediction model in different solvent is needed, which can be obtained from QM calculations. Austin et al. [48] used QM to directly predict the reaction rate in different solvents for reaction solvent design, Franco et al. [27] employed DFT calculations for the design of rechargeable batteries, Carter [58] reviewed material design using model-based approaches, including quantum mechanics. Currently, QM methods are applied in product design mainly for property prediction. However, the computation speed and the accuracy restrict the application of QM to CAMD. One of the future directions of QM for CAMD could be the integration of machine learning methods as a semi-empirical QM method to accelerate the time-consuming QM calculation for big molecules. Challenges for such machine learning models include finding better descriptors for chemical products and developing novel machine learning models tailor-made for chemical product design. Molecular dynamics (MD). Like QM, MD also provides property prediction results through its molecular scale simulation results, which can be used directly in product design, or to establish QSPR models. Similarly, in the generic problem formulation (Table 3), when the www.sciencedirect.com

Chemical product design – recent advances and perspectives Zhang et al. 29

Table 3 Generic problem formulation of chemical product design model No.

Equation

1

min=max F obj ¼ F CTY þ f ðxÞ

2

b1 ðxÞ ¼ 0

3

b2 ðx; Y Þ ¼ 0

4

b3 ðx; Y Þ  u ¼ 0

5

l1  g1 ðx; Y Þ  u1

6

l2  g2 ðx; Y Þ  u2

7

l3  p1 ðx; Y Þ  u3

n

o

Explanation

Example

Objective function. Minimizing or maximizing a certain required function. CTY represents the linear part, f ðxÞ represents linear/nonlinear part. x  0 represents a vector of continuous variables for product and/or process design variables. Equality constraints related to specifications for process design.

Maximizing product performance; minimizing cost; and so on.

Equality constraints related to the process model equations. Y ¼ 0 or 1 represents binary decision variables. Equality constraints related to the product model equations. u represents product properties. Inequality constraints related to process design specifications. Inequality constraints related to product design constraints. Design constraints related to process intensification.

property model (Eq. (4)) is missing, it is possible to use MD calculations to obtain the property values. The advantage of MD and QM methods is that properties as a function of the molecular structure can be predicted as the building blocks allow one to represent different structural differences. MD methods have already been applied to systems involving drug delivery, gas adsorption, polymer design evaluation, and so on. Al-Qattan et al. [49] designed carbon-nanotube-based targeted drug delivery using MD. Kupgan et al. [24] used MD to design  z et al. [59] polymers for CO2 capture and separations. Sled reviewed drug design using MD. However, as in the case of QM methods, the computation speed and accuracy still restrict its application to CAMD. Although several works have already been proposed to integrate MD and CAMD methods [60], these problems are not solved yet. Surrogate modeling methods derived from MD simulation can be a future direction to accelerate the integrated product design model without losing its accuracy. Group contribution (GC). Group contribution methods are some of the most commonly used property prediction methods for CAMD (see Eq. (4) in Table 3). Although, GC methods have been mainly applied to small molecules [55] and mixtures [61], recent developments have extended applications to amino acids [34], acid dissociation [62] and activity coefficients of ionic liquids [63]. Even though high computation speed and acceptable accuracy have been achieved by GC methods, there www.sciencedirect.com

Operating pressure/ temperature; utility usage; and so on. Mass and energy balance equations, and so on.

Molecular/mixture property models; and so on. Sustainability indicators; process environmental impacts; and so on. Target property constraints; and so on. Intensified equipment design variables; unit operation reduction; and so on.

Solution strategies

Direct: all equations are solved simultaneously; Indirect: Eqs. (3) or (4) are solved with local (surrogate) models in the inner loop and rigorous model off-line; Hybrid: different tools representing different subsets are solved sequentially, including candidate generation, property estimation (different subsets of linear and nonlinear), process simulation, and product performance analysis.

are limitations, such as, missing group interaction parameters, questionable accuracy for large multifunctional molecules, and inability to distinguish isomers [11]. Future directions for the GC methods include developing GC+ methods for predicting missing group parameters through lower scale property models; expanding GC methods to complex chemical systems and developing GC methods for equations of state. Thermodynamic models. Thermodynamic models are commonly used to predict phase equilibrium, specifically activity coefficients and fugacity coefficients for phase equilibrium-based separation, to evaluate product stability, and/or to predict functional properties (as a function of temperature, pressure, and/or composition). An important issue for use of thermodynamic models in chemical product design is their representation by a large number of nonlinear equations, which are difficult to solve when integrated into the product design model (see Eq. (3) in Table 3). Chao et al. [46] applied UNIFAC-IL model for extraction ionic liquid design. Scheffczyk et al. [64] developed a COSMO-CAMD framework for liquid-liquid extraction solvent design. Schilling et al. [65] developed a 1-stage CoMT-CAMD model for working fluid design of ORC process using PC-SAFT. Population-based model (PBM) and Computational fluid dynamics (CFD). PBM is based on partial differential equations that are employed to simulate dynamics of Current Opinion in Chemical Engineering 2020, 27:22–34

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particulate systems, such as crystallization, deposition, granulation, drying, polymerization, fermentation, and so on. PBM describes the time evolutions and distributions of the particulate processes by predicting particle properties, such as particle size distribution, growth and breakage. Therefore, PBM is a useful tool for combined product-process design. For example, crystallization product engineering via distribution shaping control [66], and polymerization modeling and optimization [67]. CFD is a numerical tool to analyze and solve problems that involve fluid flows to study, for example, the effect of velocity, pressure, temperature and density of a moving fluid. As in specific chemical products, fluids are often involved in the product synthesis step, and/or product application process, CFD tools could be used to evaluate the fluid flow-related functions. That is, CFD is used more in process design issues related to product design. For example, a crystallizer design problem [68], a fluidized bed polymerization reactor design problem [69] can benefit from the use of CFD tools. Two of the challenges for the application of PBM-based and CFDbased methods in product design problems are the size and complexity of the models, including large number of nonlinear/nonconvex equations, which may cause difficulties in modeling and solution of the model equations. Another challenge is to understand the principal phenomena and mechanisms involved in the process (coagulation, aggregate and breakage, etc.) as well as their dependence on parameters namely flow regime, particle size, solids concentration, mixing effect, and so on. A good understanding is fundamental towards making an appropriate selection of the mathematical equations and obtaining a successful model that can be solved in reasonable time to give reliable and useful results [70]. Process simulation. Process simulation tools help to evaluate the processing route (process flowsheet) and its operating conditions needed to manufacture the designed chemical product as well as to evaluate the performance of the product (see Table 2). For example, to evaluate the performance of a designed solvent (single species product), a solvent-based extraction process needs to be simulated [64], while the manufacturing of the solvent in this case is evaluated through standard process simulation tools. In the case of a hand lotion design, the selection of pre-emulsification and homogenization equipment and their operating conditions [71], however, need special models that are not available in standard process simulation tools. Similarly, in design of a crystallizer operation to match a specified particle size distribution or solventbased biphasic reactor operation, special process models are needed for evaluation of the processing route. That is, the use of commercial process simulators is limited by the process models available in the simulator library and therefore, verification of the product performance, including drug delivery, protection through coatings, fuel performance, and so on, still needs to be done through Current Opinion in Chemical Engineering 2020, 27:22–34

carefully designed experiments. The main reason for this is a lack of data and theory associated with the development of process models for evaluation of the product (target) functions and their corresponding processing routes.

Perspectives Chemical products have been playing an important role in sustaining modern society. However, although its methods have been studied for several decades, it is still a research topic not fully developed and most products are still developed through experiment-based trial-and-error approaches. The systematic model and/or data-based methods and associated software tools should be able to make a major contribution to chemical product design, and thereby, significantly reduce the design and development time and cost. Some perspectives for chemical product design are given below: Development of property prediction models. The library of reliable models and property data need to be enlarged. These models can be developed from three directions: theoretical-based, data-driven and their combination. However, for some of the product types and their properties, the theoretical-based models are too complex and difficult to be implemented for a computer-aided design tool. Therefore, it is not currently possible to use them in chemical product design problems. Instead, with the availability of method and tools from data science, development of data-driven models could be considered. Multiscale modeling of chemical product design problems. Multiscale modeling methods and tools are needed for certain product design problems. For example, for designing a crystallizer, or designing a solvent for crystallization, multiscale modeling can be used for establishing a better design model with additional details, where quantum mechanics models (electrons level) are used for structural optimization and obtaining missing force field parameters for MD simulation; with the optimized molecular structure, MD models (atoms level) are used to predict the crystal growth rate; then, thermodynamic models are used to predict the solid-liquid equilibrium; on the basis of the predicted crystal growth rate, population balance model and CFD simulation are used to simulate the flow distribution as well as crystal size distribution; finally, a crystallizer is designed using process unit simulation. However, the goals of product design in future are to be able to solve problems in fast and efficient way, to accelerate the product design and development process, and to release early to the market. Therefore, effective solution algorithms need to be developed to solve the large-scale nonlinear and nonconvex optimization problems. Here, model reduction techniques could also be considered. Combination of different solution methods is also a possible direction to accelerate the solution speed. For example, using machine learning www.sciencedirect.com

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methods to replace the time-consuming QM calculation [72], and using group contribution methods to fast obtain the s-profiles of the COSMO-RS model [73]. Processing route synthesis. It is still difficult to consider simultaneously the manufacturing process (processing route and techniques) of the product together with product design. This is because of a number of reasons, such as problem size and complexity, the lack of appropriate models, and/or data [12]. In some cases, for example, refrigerant cycle [16] and solvent-based extraction [64], where the production of the chemical product is neglected (or the products are assumed to be available), but the design or selection of the product and its application process have been successfully considered. For products where the application of the product (to verify its function) is difficult to model, for example, drug delivery, systems with coating [74], emulsion formation with breakage and coalescence [71], it is still difficult to design such products with model-based approaches. However, recent progress in process modeling indicates good advances [75]. For formulated products or blends where the process to make them are usually designed through experiments (for example, soaps, detergents, etc.), the main issue is to understand how the changes in microstructure (such as droplet deformation, breakup, or coalescence, changes in crystal size or distribution, and structure realignment) effect the product properties and processing conditions [2] so that appropriate model-based solution methods could be developed. Solving practical problems of significance. Although computer-aided product design methods and tools have been developed for decades, there are only few works reported that solved practical industrial problems. Therefore, efforts are needed to promote the use of various product design tools based on different design methods for industrial problem solution. For example, Fung and Ng [76] proposed rules to select chemicals for granular products such as tablets and capsules in the form of a database search and then test them through a combination of models and experiments. An indoor air purifier is designed using heuristic rules, models and experiments [13]. It should also be pointed out that through the integration of latest model-based product design techniques, such as ProCAPD [25] and OptCAMD [44], models, heuristic-rules, as well as experimental approaches, the application range of chemical product design can be extended. At the same time, more reliable, fast and sustainable product design tools that does not need long-term maintenance, need to be developed. A chemical product simulator like a process simulator could also help so that teaching, training as well as design-analysis of at least some chemical products can be routinely done by the combined effort from academia and industry. www.sciencedirect.com

Conclusions In this perspective paper, computer-aided methods and tools suitable for chemical product design in a multidisciplinary and multiscale point of view have been discussed, and the challenges and perspectives for chemical product design have been presented. In the current state, these methods and tools mainly focus on certain types of products, such as small molecules and liquid formulations and blends. However, the research scope is continuously expanding to wider application ranges, such as larger and more complex molecules, polymers, membranes, drugs, catalysts, and many more. Integration of models and data from different disciplines has allowed to reduce the gap in the understanding of relationships between product structure and their performances. Although greater success has been achieved for chemical product design, there are still many challenges and opportunities that are yet to be resolved. Such task are as follows: a) Identification of the product needs, their translation into property targets, for all types of products; b) Management of the complexity associated with the handling of multiple scales in the chemical products that need them; c) Increasing the ability to find best processing route for a given chemical product; and d) Improving the possibility to find novel, better and more innovative chemical products faster and more reliably. To address the above issues, current and future efforts are needed to integrate methods and tools from different disciplines, adopt modeling from a multiscale point of view, and encourage collaboration between academia and industries. The following are some insights needed to achieve the above goals.  Better understanding of the principles governing product properties and functions for various types of chemical products. The multidisciplinary nature of the design problem and the interaction between scales in generating solutions must be understood.  Integrating model-based and data-driven property prediction techniques to establish quantitatively accurate product structure-property relationships.  Developing multiscale approaches, which provide opportunities to generate better solutions when data and models are missing in the literature, and experiments cannot be performed for safety or other reasons.  Developing hybrid systems involving data, heuristics, model-based methods and experiments into a generic framework through problem-specific design workflows.

Although designing better and more versatile products will continue to be a challenge, and the product portfolio that can be handled by currently available computeraided tools need to be expanded. With continued developments in multidisciplinary theories, computing Current Opinion in Chemical Engineering 2020, 27:22–34

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technologies, mathematical models and algorithms, the prototype chemical product simulator (ProCAPD), which is comparable to the existing process simulators need to be further developed, so that it is possible to design rapidly and efficiently better products safer, have higher quality, and are more sustainable and environmentfriendly, and verified through focused experiments.

This perspective paper gives a brief overview on the state of the art in group-contribution-based property estimation methods and their further development and use.

Conflict of interest statement

14. Portehault D, Delacroix S, Gouget G, Grosjean R, ChanChang THC: Beyond the compositional threshold of nanoparticle-based materials. Acc Chem Res 2018, 51:930-939.

Nothing declared.

Acknowledgements Lei Zhang, Haitao Mao and Qilei Liu are grateful for the financial support of National Nature Science Foundation of China (21808025).

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