Elsevier

Computers & Chemical Engineering

Volume 116, 4 August 2018, Pages 37-55
Computers & Chemical Engineering

Computer aided chemical product design – ProCAPD and tailor-made blended products

https://doi.org/10.1016/j.compchemeng.2018.03.029Get rights and content

Highlights

  • ProCAPD, a computer aided chemical product design software tool is presented.

  • Large collection of data, models and calculation methods are available through ProCAPD.

  • ProCAPD includes design options for single species products, blended products, liquid formulated products and functional products.

  • Results from four blend design problems covering fuels and lubricants are presented.

Abstract

In chemical product design, application of computer-aided methods helps to design as well as improve products to reach the market faster by reducing time-consuming experiments at the early stages of design. That is, experiments are performed during the later stages as a verification or product refinement step. Computer-aided molecular and mixture-blend design methods are finding increasing use because of their potential to quickly generate and evaluate thousands of candidate products; to estimate a large number of the needed physico-chemical properties; and to select a small number of feasible product candidates for further verification and refinement by experiments. In this paper, an extended computer-aided framework and its implementation in a product design software tool is presented, highlighting the new features together with an overview on the current state of the art in computer-aided chemical product design. Results from case studies involving tailor-made blend design are presented to highlight the latest developments.

Introduction

Product design and development has become an integral topic within the domain of chemical engineering as society is entering into an era of increasing focus on high value-added products, green chemistry and product-process sustainability (Hill, 2009). In chemical product design and development, it is not only important to find the chemical product that exhibits certain desirable properties but finding a way to manufacture it, to improve its performance and to make it more versatile is receiving increased attention (García-Serna et al., 2007). Chemical products can be classified as (Gani and Ng, 2015, Seider et al., 2017): (a) single species products such as commodity chemicals (solvents, refrigerants) and active ingredients (AI) that are used in formulated-functional products (surfactants, amino acids); (b) multiple-species products such as blends (tailor-made fuels and lubricants) that contain more than one chemical to perform the desired product functions; (c) formulated products such as cosmetics, cream and detergents that are also multiple species products but contain an AI together with additives to improve the quality of the product and/or deliver the product to a desired location (such as surface, human skin, or organ); and, (d) devices such as humidifiers, air filters and controlled release capsules that are used to measure, make, purify, or transform-transfer chemicals for a purpose.

Among these chemical products, research in the area of tailor-made blends, such as fuels for internal combustion (IC) engines is receiving much attention as they are suitable for applications of computer-aided design techniques. Detailed chemical kinetic models enable simulation of the combustion of transportation fuels such as gasoline, diesel and jet fuels, which provide guidelines to improve the engine performance while reducing pollutant emissions. However, fuels that are derived from conventional petroleum sources are composed of hundreds to thousands of compounds. Representation of these fuels as model mixtures containing all the compounds is neither practical nor feasible, unless the actual fuel is used. In order to overcome this difficulty, it has been proposed to represent fuels with models (also called surrogate fuels) that can be used for experiments as well as calculations of fuel properties (Gauthier et al., 2004). A surrogate fuel is a mixture of a smaller set of compounds that is designed to match certain characteristics of a target fuel containing many compounds (Pitz and Mueller, 2011). Both chemical as well as physical properties (such as combustion and injection, vaporization and mixing characteristics) need to be matched by the surrogate fuel in order to produce the same performance of the target fuel. Additives added to the surrogate fuel to improve the performance of the final fuel product are called tailor-made fuel (surrogate) blends.

Lubricants are also mixtures of chemicals but they are employed to reduce friction between moving parts, to minimize component wear, to remove heat, to neutralize and disperse combustion products, to prevent rust and corrosion and to prevent sludge formation or deposits. Single chemical lubricants may not be able to provide optimum protection against all damage conditions (Jannin et al., 2003). Therefore, lubricants (original or surrogate) are typically blended with additives and/or other lubricants to match desired specifications. These lubricant blends are called in this paper, “tailor-made lubricants”.

Like the CAMD (Computer Aided Molecular Design) problems, CAMbD (Computer Aided Mixture-blend Design) problems can also be formulated in Mixed Integer Non-Linear Programming (MINLP) or Mixed Integer Linear Programming (MILP) modes (Duvedi and Achenie, 1996, Zhang et al., 2015) and solved in different ways (Zhang et al., 2017). CAMbD is defined as follows: given a set of chemicals and a set of property constraints, determine the optimal mixture and/or blend consisting of a sub-set of chemicals (Gani, 2004). The identities of the chemicals and their relative amounts in the final mixture (blend) are unknown at the start, but the molecular structures of the candidate chemicals are known together with the target properties of the blend. In this way, CAMbD problems are extensions of CAMD problems. That is, while a CAMD problem is formulated by giving the specifications (target properties) of the desired product (chemical) and solved by determining the molecular structures of the chemicals that satisfy the target properties, a CAMbD problem is considered when single molecules are unable to satisfy all the desired product specifications (Gani, 2004). By employing computer aided tools, the CAMbD approach is able to identify a smaller set of promising candidates by screening numerous blend alternatives.

Related to CAMbD, significant efforts have been directed to the design of solvent mixtures (Buxton et al., 1999, Sinha et al., 2003, Karunanithi et al., 2005, Jonuzaj et al., 2016), polymer blends (Vaidyanathan and El-Halwagi, 1996, Solvason et al., 2009) and formulated liquid products (Conte et al., 2011); and more recently, liquid-fuel blends (Cholakov, 2011; Yunus et al., 2014, Zhang et al., 2015, Choudhury et al., 2017). Different solution strategies have been employed to solve CAMbD problems. Marini et al. (2007) employed “Artificial Neural Networks (ANN)” to determine binary blends of mono-cultivar Italian olive oils. Ordouei et al. (2015) employed “Analytical Hierarchy Process (AHP)” to screen and select the best gasoline blends. Dahmen and Marquardt (2016) employed a novel rule-based generator of molecular structures to resemble carbon- and energy- efficient biofuels. Papadopoulos et al. (2013) and Zhang et al. (2015) formulated and solved MINLP problems to design working fluid mixtures for Organic Rankine Cycles, while, Jonuzaj et al. (2016) did the same for solvent mixture design. Yunus et al. (2014) decomposed the overall MINLP problem into sub-problems for tailor-made design of gasoline and lubricant blends.

Uses of experimental trial and error-based chemical product design approaches are well-known (Cussler and Moggridge, 2011). However, in order to avoid long development times and high consumption of experimental resources, this approach should be employed when there are not many candidates or when the important end-use properties can only be verified by experiments. Therefore, CAMbD is an appropriate alternative approach when used with validated mathematical models for the estimation of target properties. Note, however, all CAMD and CAMbD techniques employ model-based computer-aided solution approaches.

As the performance of the model-based computer-aided method depends on the reliability of the data and the property models used, on the assumptions made to model the product performance, and, the hypotheses employed to transform the product needs-functions to target properties, the application of any model-based system has limitations. In addition, models may not be able to provide estimates for certain classes of target properties such as odor, color, or oxidation stability. Therefore, in order to overcome the uncertainty and application range of the model-based chemical product design method, the use of an integrated experimental computer-aided design approach is highlighted in this paper, similar to Conte et al. (2012) for liquid formulated products. Only extensions related to CAMbD are highlighted in this paper (extension of solution approaches for CAMD problems can be found in Zhang et al., 2015). For CAMbD problems, two calculation modes are considered: (a) an extended generate and test mode (Yunus et al., 2014); (b) two-step mathematical programming mode. The objective of both calculation modes is to identify a small number of candidates that could be further investigated through more rigorous models, collected data and/or experiments. Therefore, the search space is reduced and, time and resources are spared. The expensive experiments are reserved only for the most promising candidates (Gani, 2004). This approach needs a flexible computer aided framework through which in-house data, models and design methods can be employed to solve a wide range of not only CAMbD problems but also design of other types of chemicals-based products, for example, single species molecular products (solvents, surfactants, amino acids) and liquid formulated products (lotions, insect repellents). A combination of tools is needed for design of specific products, for example, property prediction tools are needed to calculate target properties and numerical solvers (optimizer) are needed to formulate and solve the product design problem as an optimization problem. These tools need to be systematic but flexible, simple but accurate and the models should be predictive to cover a wide range of application.

The objective of this paper is to present an extended computer-aided framework and its implementation in a chemical product design software tool (Kalakul et al., 2017b) as a hybrid computer-aided framework capable of handling many types of chemical product design problems. The new version of the chemical product design software is called ProCAPD. The objective of the extended framework with its associated data, models, methods and tools is to provide the means to design, analyze, and verify a wide range of chemical products in a fast, efficient, reliable and systematic manner. Compared to the earlier version, ProCAPD now has an improved software architecture, new and extended databases, new and extended models in the model library and new solution approaches. For example, for CAMbD problems, new property models for prediction of Cetane Index, pour point, carbon dioxide emission from the combustion engines and friction coefficient for lubricants have been added. The pure compound group contribution property models have been updated with new parameters and databases for lipids, solvents and amino acids have been extended and verified for consistency. Thus, the ProCAPD software is able to solve wider ranges of not only CAMbD problems in a fast and efficient way but also solve a wider range of other CAMD and liquid formulation problems.

The rest of the paper is organized as follows: in Section 2, the product needs versus property models for different types of blended products are discussed in three subsections (translation of needs to properties; pure component property models and mixture property models; and blend stability tests); in Section 3, the new CAMbD option within ProCAPD is presented with detailed step-by-step description of the design method; in Section 4 an overview of the ProCAPD software tool is given; in Section 5, application of the new CAMbD option is highlighted for three illustrative case studies; in Section 6 conclusions and future work are given. In addition to the above, in appendix, the solution of the two-step algorithm (for CAMbD problems) is highlighted through a conceptual example. Also, as supplementary material, additional details of the three case studies are given together with screen-shots from the ProCAPD software tool highlighting the solution of other product design problems and additional details of the software architecture. Throughout this paper, the term “surrogate” is used to refer to the original fuel represented by a smaller number of compounds; the term “surrogate blend” is used to refer to tailor-made surrogates plus additives. The term “ingredients” is used to refer to candidate chemicals that may be considered to represent the surrogate and/or addition to the surrogate blend. Fuels as well as lubricants are covered by the term “surrogate blends”.

Section snippets

Blended products: product needs – property models

Three issues are highlighted in this section: translation of product needs to target properties; the models used for estimation of the target properties and the blend stability test calculation option for tailor-made design of single phase liquid formulations that includes blends (for example, fuels and lubricants) as well as household products (for example, detergents, hair sprays and insect repellents). Although these issues are highlighted for blended products, the same concepts are also

CAMbD option within ProCAPD: overview

The framework (architecture) of the CAMbD option in ProCAPD is highlighted in Fig. 2 in terms of the main steps (work-flow), the methods and tools employed in each step and the data-flow from one step to the next. The overall framework covering all types of chemical products is given as supplementary material (see Sections S3 and S4). There are 4 main steps where step-1 formulates the type of CAMbD problem; step-2 creates a list of chemicals with their associated properties and retrieves the

ProCAPD overview

The architecture of the ProCAPD software-tool is based on a collection of design methods (CAMD for single molecule products, CAMbD, liquid formulation design, emulsified product design and device design) and associated tools, models and data, thereby, making it a versatile chemical product design tool in the same way as a process simulation tool. The advantage here is that one can design as well as evaluate while in a process simulation tool, one can usually only evaluate through simulation

Case studies

The chemical product design template provides the work-flow according to the systematic design framework implemented in ProCAPD, the needed solution methods, the associated computational tools, and the information that come from different sources and disciplines. In this way, users are able to simultaneously and efficiently use the template in order to design/verify, for example, the tailor-made design of surrogates and/or surrogate blends. Four case studies involving the tailor-made design of

Conclusion

The development and use of the tailor-made blend design template that has been specially highlighted in this paper should be able to help users to define, solve and/or analyze different types of blended products. The implemented framework for the blend design template provides the work-flow and associated models, data and tools for four types of blend design problems. The application of the blend design template is highlighted through case studies involving tailor-made surrogate fuels

Acknowledgment

The authors are grateful for the financial support of “the Fundamental Research Funds for the Central Universities DUT17RC(3)008”, NSFC (21576036) and NSFC (21776035). This publication was made possible by NPRP award [NPRP 5-066-2-023] from the Qatar National Research Fund, which is member of the Qatar Foundation. The statements made herein are solely the responsibility of the authors.

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      Product design software is based on computer-aided molecular design method, which helps to quickly realize solvent screening/design. Kalakul et al. [95] presented a new version of the product design software tool ProCAPD. Compared with earlier versions, ProCAPD has improved software architecture, new and extended databases, models in the model library and new solution approaches.

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