- Email: [email protected]
The Chemical Product Simulator – ProCAPD
Antonio Espuña, Moisès Graells and Luis Puigjaner (Editors), Proceedings of the 27th European Symposium on Computer Aided Process Engineering – ESCAPE 27 October 1st - 5th, 2017, Barcelona, Spain © 2017 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/B978-0-444-63965-3.50165-3
The Chemical Product Simulator – ProCAPD Sawitree Kalakula, Mario R. Edena* , Rafiqul Ganib a
Department of Chemical Engineering, Auburn University, Auburn, AL 36849, USA
b
Department of Chemical and Biochemical Engineering, Soltofts Plads, Building 229, DK-2800 Kongens Lyngby, Denmark [email protected]
Abstract In this paper, a chemical product design simulator called ProCAPD is presented. ProCAPD works in the same way as a chemical process simulator, that is, it helps to verify product design decisions and generates information that can be used to make design decisions. Like the contents of the process simulator, the product simulator needs a database of chemicals and properties, a library of models, numerical routines to solve mathematical problems as well as various calculation options. Also, like the process simulator, the product simulator comes with a user-interface to describe the problems and to obtain the simulation results. In order to make the chemical product simulator versatile and applicable for a wide range of problems, it includes a suite of databases (chemicals, solvents, active ingredients, aroma, color-agents and many more); a library of models (properties, product performance, etc.); calculation tools (product attributes, blend compositions, environmental impact, etc.); design templates (single molecules, blends, formulations, emulsions, devices); and, design-simulation-analysis functions. All these capabilities are based on the prototype tool developed by Kalakul et al. (2017). This paper highlights the software architecture, the implemented computer aided methods-tools, whose scope-significance are illustrated through new chemical product design-evaluation applications. Keywords: Chemical product design, Product-process simulator, blended products, formulations, single molecular products.
1. Introduction Product design and development has become a key topic in the development of chemical engineering as a profession since chemical industries have entered into the era of increasing focus on high value added products, green chemistry and product-process sustainability (Hill et al., 2009). In chemical product design and development, it is not only important to find the chemical product that exhibits certain desirable properties and find the way to manufacture it but also improving the product performance and making the products more versatile have received increased attention in recent times. It is now generally accepted that applications of computer-aided model-based methodologies help to design/improve products to reach the market faster by reducing some costly and timeconsuming experiments (Gani, 2004) and by employing valuable resources related to experiments only during the final (verification) stage. As the product design problems become more complex due to the chemical systems involved, more time and effort is needed for their solution. Also, through the currently
980
S. Kalakul et al.
available methods and tools, only a small percentage of product design problems can be solved due to lack of the needed property models, data and the multidisciplinary nature of many chemical product design problems. Process needs and sustainability issues possess demanding constraints as well and the problem complexity increases because the relationships between product property, sustainability and process criteria are difficult to mathematically define. This is a challenging task requiring data acquisition, data testing, model development and multi-scale modeling needs to be integrated in a computer-aided framework (Zhang et al., 2016). The objective of this paper is to design, analyze and verify chemicals based products in a fast, efficient and systematic manner. The development of a new product design simulator based on the prototype tool developed by Kalakul et al. (2017) is presented together with implementation aspects of new optimization tools for blend design, additional property models and product design templates for a wider range of products.
2. Architecture (framework) of ProCAPD The integration and merging of methods and tools from different sources have been established through the use of COM (component object model) technology. The systematic architecture for implementation into the product design simulator called ProCAPD is shown in Figure 1 where it can be seen that the software handles 4 main product design related problems (modules). Each module is characterized by its options, algorithms, and tools. Module
Option
Algorithm
Tool
Pure compound database Property (data,model)
Model Development
ProCAPD Product Design
Product Analysis
Database search User defined database
New model generation Modify existing model Model validation New template generation Molecular design Formulation design Emulsion design Blend design Device design
Mixture database Property models Product performance models Product-process models Database generation Model description Model solution Modeling templates Model consistency test Template for new products Database search Solvent design Generate and test Mathematical programming Conte et al., 2012 Kalakul et al., 2017 Kalakul et al., 2017 Kalakul et al., 2017
ProCAPD Database Database generation toolbox Modeling toolbox Property toolbox Template generation toolbox ProCAPD Database Solvent design toolbox CAMD toolbox Mathematical programming toolbox Modeling toolbox
Product specification analysis
Product performance calculation
Formulation design template Emulsion design template Blend design template Device design template
Product property calculation
Product experimental verification
Property toolbox
Design of experimental verification
Experimental toolbox
Figure 1: Architecture of ProCAPD. 2.1. Property (models/data) This module stores experimental and predicted property data. It has an option to search for pure and mixture property data for a very wide range of chemicals (more than 26,000 compounds) classified in terms of use in chemical products such as lipids, emulsions,
The Chemical Product Simulator – ProCAPD
981
normal fluids, polymers, amino acids, formulations, combustible compounds for fuel blends. In case experimental data is not available, property models (pure and mixture properties) are used to fill the gap. A model library in the ProCAPD database stores 31 models for primary, 10 for secondary, 19 for functional properties and models for more than 20 mixture properties. The user interface guides the user to select compounds and properties and/or performances that need to be calculated for the product of their choice. Furthermore, users are able to create their own database by using the database generation toolbox to manage information through the ProCAPD database ontology. Model consistency test options are used to check the consistency of retrieved models from the database and/or predicted data through property models. 2.2. Model development This module is designed for generation, analysis and validation of new models for product design and analysis tasks that cannot be handled with those currently available in the ProCAPD model library. Through the use of a modelling toolbox, it is possible to quickly create, validate and add new models to the model library. A modeling template is used for model-reuse, that is, take an existing model and modify it to match the objectives of the new model. 2.3. Product design This module has options to design a wide range of chemical product types, as classified by Gani and Ng (2015): (1) single molecular products, (2) formulations, (3) emulsions, (4) blends and (5) devices. Each option consists of a number of problem specific templates (methodologies) together with their corresponding database, solvers, product design algorithms and analysis tools. The solutions of these problems require data, models (when all necessary data is not available) and solvers (solution strategies for the mathematically formulated design problem). Two main classes of solution strategies are employed: the decomposition based generate-test approach (Harper et al. 2000) and the mathematical programming approach (Zhang et al. 2015). A unique feature of the software is the use of the problem specific templates employed through the generic design workflow (Figure 2) to simplify the use of property models and product-process design methods and algorithms.
Figure 2: Generic workflow for product design module. In step 1, the product-process design problem is defined through the definition of needs and their translation to target properties for the desired products. In step 2, the computeraided product design problem is formulated through molecular structure constraints, mixture constraints, property constraints and process constraints. Here, knowledge base, property models, thermodynamic models and process models are also available, if needed. In step 3, the design problem is formulated as a mathematical programming
982
S. Kalakul et al.
problem and solved using built-in solvers and/or links to external solvers in GAMS and MATLAB (usually for MINLP problems). Different solution strategies are available to solve the design problem, such as, generate-test approach (first generate candidates that satisfy the constraints and then order the feasible candidates in terms of their objective function value); simultaneous solution approach (use an appropriate optimization algorithm to solve the MINLP problem); decomposition approach (decompose the main problem into sub-problems and solve these sub-problems according to a predefined solution order). Note that it is possible to consider economic, environmental and sustainability issues through the addition of appropriate models. In step 4, each feasible product design is verified either through rigorous model-based tests or experimental tests suggested by the design of experiments (DOE) toolbox. In this step, the stability of the product, the desired performance, the properties, the color, the smell, etc., are verified. 2.4. Product analysis Product specification analysis is used for a known product whose properties and/or performance need to be tested and/or verified. The property toolbox is used to calculate pure compound properties and/or product performance properties such as controlled release of active ingredients in capsules, solvent evaporation rate and uptake of active ingredients. Furthermore, designed products can be experimentally verified by using an experimental toolbox for guidelines in order to design experiments.
3. ProCAPD application examples The application examples are highlighted through the use of design templates for design of tailor-made jet-fuels and microcapsules for controlled release of a pesticide. 3.1. Tailor-made jet-fuels design The objective of this example is to find the promising additives to be blended with the Synthetic Paraffinic Kerosene (SPK) that improve the jet-fuel properties (Jet A-1) and benefits in terms of energy diversity thus reducing dependence on petroleum crude oil. The composition of the SPK as a main ingredient (MI) is provided by TEES Gas & Fuels Research Center, Texas A&M University at Qatar, 23874 Doha, Qatar. Step 1: Problem Definition – The new formulation of jet-fuel blends should have good fuel performance and meet or exceed stringent requirements for worldwide fuel handling and products standards as listed in Table 1. Table 1. Product needs translated target properties Need
Target properties
Melting point (K) Lower heating value (MJ/kg) Flash point (K) Reid vapor pressure (kPa) Density at 15 C (kg/m3) Kinematic viscosity at -20 C (cst) -logLC50 (mol/L ) Carbon dioxide emission (kgCO2/mile)
Tm < 221.15 LHV > 42.8 Tf > 311.15 RVP < 1 !ȡ! V<8 -logLC50 < 4.58 CO2E < 25.36
The Chemical Product Simulator – ProCAPD
983
Step 2: Problem formulation – A set of feasible additives is generated using the Computer Aided Molecular Design (CAMD) technique (Harper et al. 2000). Thousands of chemicals are screened through the pure component constraint of molecular weight, which is reduced to 50 chemicals based on the knowledge base and existing products as a benchmark; pure component properties listed in Table 1. Missing properties are estimated through the property toolbox. CO2 emission is estimated through a model available at USEPA (Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 – 2005, 2007). Step 3: MI(N)LP formulation – The type of blend to be designed, the objective function and the optimization problem type need to be defined. The product design problem is formulated as a MINLP problem using the decomposition approach, where the MI composition is minimized, subject to target properties. Initially, the software lists 50 chemicals as feasible additives, which can formulate 1,225 ternary mixtures (MI + additive(1) + additive(2)). 903 ternary mixtures are excluded due to their density. The remaining 322 ternary mixtures that are miscible with MI lead to the generation of 644 alternatives at different compositions. 12 alternatives are left which are evaluated through mathematical programming (to find the optimal mixture compositions) with linear and/or non-linear property constraints. The objective function is to maximize the jet-fuel main ingredient (MI). The linear target property constraints are: LHV, V, CO2( ȡ DQG logLC50, while the non-linear constraint is: RVP. Finally, the most promising ternary blends with the maximum MI composition are listed in Table 2 with target properties values. Blend 2 is the most promising blend since the blend could maximize the consumption of MI as well as reduce the toxicity and carbon dioxide emission from engine combustion. The carbon dioxide emission is 21.1% less than a conventional jetfuel (note that the average jet-fuel has a CO2 emission of 25.37 kgCO2/mile (Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 – 2005, 2007))). In addition, the volumetric heating value is improved by 2%. Table 2. Mixtures matching the target properties and their estimated property values Blend ID
1 2 3
Composition (%Vol) MI(100) MI(80.2)+ Decalin(8.3) + Butylbenzene (11.5) MI(78.6)+ Decalin(12.4) + Pentylbenzene(9) MI(77)+ Decalin(6) + Hexylbenzene(17)
LHV
Tf
RVP
ȡ
V
44.29
320.9
0.64
756.15
3.22
logLC50 4.58
43.54
318.94
0.57
784.21
3.55
4.41
21.41
43.49
319.32
0.55
785.84
4.22
4.32
20.02
43.48
320.3
0.52
784.18
3.94
4.51
22.12
CO2E 22.92
Step 4: Model-based/Experimental verification – Flash point (Tf) property model requires an iteration to obtain the flash point of the mixture, thus it is only used for the blends from Step 3 that have been shortlisted (see Table 2). Therefore, Tf of all blends are higher than MI and satisfy aviation Jet-A1 standards. Furthermore, the experimental toolbox suggests H[SHULPHQWDOWHVWVWRYHULI\9ȡ593GLVWLOODWLRQSURILOHVDQG-)727ǻ3DWºC to ensure that the final blends meet the aviation fuel standards based on these properties.
984
S. Kalakul et al.
3.2. Microcapsules for controlled release of a pesticide design A controlled release device is used to deliver pesticides to crops in order to optimize the delivery of a pesticide and reduce hazards to humans and the environment. The device consists of a pesticide (permethrin) as an active ingredient (AI) that is encapsulated within a polymeric membrane (poly butyl-methacrylate), which controls the amount of the AI to be diffused out into water (the most common release medium in the agrochemical field). In this example, the device template is used to find a donor medium that is able to achieve the target behavior. The target product performance is set at 90 % release of the permethrin to water in 3 hours. The calculation of the needed properties such as diffusivity coefficient between AI and polymer, critical volume, molecular weight and glass temperature of polymer are performed via the property toolbox. The results from the calculation of the controlled release model using different donor mediums indicate that it is possible to achieve 95% release from the capsule in 3 hours by using n-hexane as the donor medium.
4. Conclusion A computer-aided framework for design of chemical products has been developed and used as the architecture for the ProCAPD software. The model libraries, the structured databases and the generic workflow are integrated through the product design ontology developed to represent the associated knowledge. The use of the framework has been highlighted through 2 representative case studies involving tailor-made jet-fuels and microcapsules for controlled release of a pesticide. It helps to reduce the search space and provides promising chemical candidates that are competitive as well as economic and environmentally feasible, satisfying product performance specifications and making it more flexible and capable of solving a wide range of product design problems.
References E. Conte, R. Gani, T.I. Malik, 2011, The virtual Product-Process Design laboratory to manage the complexity in the verification of formulated products, Fluid Phase Equilibria, 302, 294-304. R. Gani, 2004, Chemical Product Design: Challenges & Opportunities, Computers and Chemical Engineering, 28, 2441-2457. R. Gani, K. M. Ng, 2015, Product design – Molecules, devices, functional products, and formulated products, Computers & Chemical Engineering, 81, 70-79. P. M. Harper, R. Gani, 2000, A multi-step and multi-level approach for computer aided molecular design, Computers & Chemical Engineering, 24, 677-683. M. Hill, 2009. Chemical Product Engineering – The third paradigm, Computer & Chemical Engineering, 33(5), 947-953. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 – 2005, 2007, EPA430-R-07-002, U.S. Environmental Protection Agency, Washington, United States of America. S. Kalakul, S. Cignitti, L. Zhang, R. Gani, 2017, Chapter 3 – VPPD-Lab: The Chemical Product Simulator, Tools For Chemical Product Design, Computer Aided Chemical Engineering, 39, 61-94. L. Zhang, D. K. Babi, R. Gani, 2016, New Vistas in Chemical Product and Process Design, Annual Review of Chemical and Biomolecular Engineering, 7, 557-582. L. Zhang, S. Cignitti, R. Gani, 2015, Generic mathematical programming formulation and solution for computer-aided molecular design, Computers & Chemical Engineering, 78, 79-84.
The Chemical Product Simulator – ProCAPD Sawitree Kalakula, Mario R. Edena* , Rafiqul Ganib a
Department of Chemical Engineering, Auburn University, Auburn, AL 36849, USA
b
Department of Chemical and Biochemical Engineering, Soltofts Plads, Building 229, DK-2800 Kongens Lyngby, Denmark [email protected]
Abstract In this paper, a chemical product design simulator called ProCAPD is presented. ProCAPD works in the same way as a chemical process simulator, that is, it helps to verify product design decisions and generates information that can be used to make design decisions. Like the contents of the process simulator, the product simulator needs a database of chemicals and properties, a library of models, numerical routines to solve mathematical problems as well as various calculation options. Also, like the process simulator, the product simulator comes with a user-interface to describe the problems and to obtain the simulation results. In order to make the chemical product simulator versatile and applicable for a wide range of problems, it includes a suite of databases (chemicals, solvents, active ingredients, aroma, color-agents and many more); a library of models (properties, product performance, etc.); calculation tools (product attributes, blend compositions, environmental impact, etc.); design templates (single molecules, blends, formulations, emulsions, devices); and, design-simulation-analysis functions. All these capabilities are based on the prototype tool developed by Kalakul et al. (2017). This paper highlights the software architecture, the implemented computer aided methods-tools, whose scope-significance are illustrated through new chemical product design-evaluation applications. Keywords: Chemical product design, Product-process simulator, blended products, formulations, single molecular products.
1. Introduction Product design and development has become a key topic in the development of chemical engineering as a profession since chemical industries have entered into the era of increasing focus on high value added products, green chemistry and product-process sustainability (Hill et al., 2009). In chemical product design and development, it is not only important to find the chemical product that exhibits certain desirable properties and find the way to manufacture it but also improving the product performance and making the products more versatile have received increased attention in recent times. It is now generally accepted that applications of computer-aided model-based methodologies help to design/improve products to reach the market faster by reducing some costly and timeconsuming experiments (Gani, 2004) and by employing valuable resources related to experiments only during the final (verification) stage. As the product design problems become more complex due to the chemical systems involved, more time and effort is needed for their solution. Also, through the currently
980
S. Kalakul et al.
available methods and tools, only a small percentage of product design problems can be solved due to lack of the needed property models, data and the multidisciplinary nature of many chemical product design problems. Process needs and sustainability issues possess demanding constraints as well and the problem complexity increases because the relationships between product property, sustainability and process criteria are difficult to mathematically define. This is a challenging task requiring data acquisition, data testing, model development and multi-scale modeling needs to be integrated in a computer-aided framework (Zhang et al., 2016). The objective of this paper is to design, analyze and verify chemicals based products in a fast, efficient and systematic manner. The development of a new product design simulator based on the prototype tool developed by Kalakul et al. (2017) is presented together with implementation aspects of new optimization tools for blend design, additional property models and product design templates for a wider range of products.
2. Architecture (framework) of ProCAPD The integration and merging of methods and tools from different sources have been established through the use of COM (component object model) technology. The systematic architecture for implementation into the product design simulator called ProCAPD is shown in Figure 1 where it can be seen that the software handles 4 main product design related problems (modules). Each module is characterized by its options, algorithms, and tools. Module
Option
Algorithm
Tool
Pure compound database Property (data,model)
Model Development
ProCAPD Product Design
Product Analysis
Database search User defined database
New model generation Modify existing model Model validation New template generation Molecular design Formulation design Emulsion design Blend design Device design
Mixture database Property models Product performance models Product-process models Database generation Model description Model solution Modeling templates Model consistency test Template for new products Database search Solvent design Generate and test Mathematical programming Conte et al., 2012 Kalakul et al., 2017 Kalakul et al., 2017 Kalakul et al., 2017
ProCAPD Database Database generation toolbox Modeling toolbox Property toolbox Template generation toolbox ProCAPD Database Solvent design toolbox CAMD toolbox Mathematical programming toolbox Modeling toolbox
Product specification analysis
Product performance calculation
Formulation design template Emulsion design template Blend design template Device design template
Product property calculation
Product experimental verification
Property toolbox
Design of experimental verification
Experimental toolbox
Figure 1: Architecture of ProCAPD. 2.1. Property (models/data) This module stores experimental and predicted property data. It has an option to search for pure and mixture property data for a very wide range of chemicals (more than 26,000 compounds) classified in terms of use in chemical products such as lipids, emulsions,
The Chemical Product Simulator – ProCAPD
981
normal fluids, polymers, amino acids, formulations, combustible compounds for fuel blends. In case experimental data is not available, property models (pure and mixture properties) are used to fill the gap. A model library in the ProCAPD database stores 31 models for primary, 10 for secondary, 19 for functional properties and models for more than 20 mixture properties. The user interface guides the user to select compounds and properties and/or performances that need to be calculated for the product of their choice. Furthermore, users are able to create their own database by using the database generation toolbox to manage information through the ProCAPD database ontology. Model consistency test options are used to check the consistency of retrieved models from the database and/or predicted data through property models. 2.2. Model development This module is designed for generation, analysis and validation of new models for product design and analysis tasks that cannot be handled with those currently available in the ProCAPD model library. Through the use of a modelling toolbox, it is possible to quickly create, validate and add new models to the model library. A modeling template is used for model-reuse, that is, take an existing model and modify it to match the objectives of the new model. 2.3. Product design This module has options to design a wide range of chemical product types, as classified by Gani and Ng (2015): (1) single molecular products, (2) formulations, (3) emulsions, (4) blends and (5) devices. Each option consists of a number of problem specific templates (methodologies) together with their corresponding database, solvers, product design algorithms and analysis tools. The solutions of these problems require data, models (when all necessary data is not available) and solvers (solution strategies for the mathematically formulated design problem). Two main classes of solution strategies are employed: the decomposition based generate-test approach (Harper et al. 2000) and the mathematical programming approach (Zhang et al. 2015). A unique feature of the software is the use of the problem specific templates employed through the generic design workflow (Figure 2) to simplify the use of property models and product-process design methods and algorithms.
Figure 2: Generic workflow for product design module. In step 1, the product-process design problem is defined through the definition of needs and their translation to target properties for the desired products. In step 2, the computeraided product design problem is formulated through molecular structure constraints, mixture constraints, property constraints and process constraints. Here, knowledge base, property models, thermodynamic models and process models are also available, if needed. In step 3, the design problem is formulated as a mathematical programming
982
S. Kalakul et al.
problem and solved using built-in solvers and/or links to external solvers in GAMS and MATLAB (usually for MINLP problems). Different solution strategies are available to solve the design problem, such as, generate-test approach (first generate candidates that satisfy the constraints and then order the feasible candidates in terms of their objective function value); simultaneous solution approach (use an appropriate optimization algorithm to solve the MINLP problem); decomposition approach (decompose the main problem into sub-problems and solve these sub-problems according to a predefined solution order). Note that it is possible to consider economic, environmental and sustainability issues through the addition of appropriate models. In step 4, each feasible product design is verified either through rigorous model-based tests or experimental tests suggested by the design of experiments (DOE) toolbox. In this step, the stability of the product, the desired performance, the properties, the color, the smell, etc., are verified. 2.4. Product analysis Product specification analysis is used for a known product whose properties and/or performance need to be tested and/or verified. The property toolbox is used to calculate pure compound properties and/or product performance properties such as controlled release of active ingredients in capsules, solvent evaporation rate and uptake of active ingredients. Furthermore, designed products can be experimentally verified by using an experimental toolbox for guidelines in order to design experiments.
3. ProCAPD application examples The application examples are highlighted through the use of design templates for design of tailor-made jet-fuels and microcapsules for controlled release of a pesticide. 3.1. Tailor-made jet-fuels design The objective of this example is to find the promising additives to be blended with the Synthetic Paraffinic Kerosene (SPK) that improve the jet-fuel properties (Jet A-1) and benefits in terms of energy diversity thus reducing dependence on petroleum crude oil. The composition of the SPK as a main ingredient (MI) is provided by TEES Gas & Fuels Research Center, Texas A&M University at Qatar, 23874 Doha, Qatar. Step 1: Problem Definition – The new formulation of jet-fuel blends should have good fuel performance and meet or exceed stringent requirements for worldwide fuel handling and products standards as listed in Table 1. Table 1. Product needs translated target properties Need
Target properties
Melting point (K) Lower heating value (MJ/kg) Flash point (K) Reid vapor pressure (kPa) Density at 15 C (kg/m3) Kinematic viscosity at -20 C (cst) -logLC50 (mol/L ) Carbon dioxide emission (kgCO2/mile)
Tm < 221.15 LHV > 42.8 Tf > 311.15 RVP < 1 !ȡ! V<8 -logLC50 < 4.58 CO2E < 25.36
The Chemical Product Simulator – ProCAPD
983
Step 2: Problem formulation – A set of feasible additives is generated using the Computer Aided Molecular Design (CAMD) technique (Harper et al. 2000). Thousands of chemicals are screened through the pure component constraint of molecular weight, which is reduced to 50 chemicals based on the knowledge base and existing products as a benchmark; pure component properties listed in Table 1. Missing properties are estimated through the property toolbox. CO2 emission is estimated through a model available at USEPA (Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 – 2005, 2007). Step 3: MI(N)LP formulation – The type of blend to be designed, the objective function and the optimization problem type need to be defined. The product design problem is formulated as a MINLP problem using the decomposition approach, where the MI composition is minimized, subject to target properties. Initially, the software lists 50 chemicals as feasible additives, which can formulate 1,225 ternary mixtures (MI + additive(1) + additive(2)). 903 ternary mixtures are excluded due to their density. The remaining 322 ternary mixtures that are miscible with MI lead to the generation of 644 alternatives at different compositions. 12 alternatives are left which are evaluated through mathematical programming (to find the optimal mixture compositions) with linear and/or non-linear property constraints. The objective function is to maximize the jet-fuel main ingredient (MI). The linear target property constraints are: LHV, V, CO2( ȡ DQG logLC50, while the non-linear constraint is: RVP. Finally, the most promising ternary blends with the maximum MI composition are listed in Table 2 with target properties values. Blend 2 is the most promising blend since the blend could maximize the consumption of MI as well as reduce the toxicity and carbon dioxide emission from engine combustion. The carbon dioxide emission is 21.1% less than a conventional jetfuel (note that the average jet-fuel has a CO2 emission of 25.37 kgCO2/mile (Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 – 2005, 2007))). In addition, the volumetric heating value is improved by 2%. Table 2. Mixtures matching the target properties and their estimated property values Blend ID
1 2 3
Composition (%Vol) MI(100) MI(80.2)+ Decalin(8.3) + Butylbenzene (11.5) MI(78.6)+ Decalin(12.4) + Pentylbenzene(9) MI(77)+ Decalin(6) + Hexylbenzene(17)
LHV
Tf
RVP
ȡ
V
44.29
320.9
0.64
756.15
3.22
logLC50 4.58
43.54
318.94
0.57
784.21
3.55
4.41
21.41
43.49
319.32
0.55
785.84
4.22
4.32
20.02
43.48
320.3
0.52
784.18
3.94
4.51
22.12
CO2E 22.92
Step 4: Model-based/Experimental verification – Flash point (Tf) property model requires an iteration to obtain the flash point of the mixture, thus it is only used for the blends from Step 3 that have been shortlisted (see Table 2). Therefore, Tf of all blends are higher than MI and satisfy aviation Jet-A1 standards. Furthermore, the experimental toolbox suggests H[SHULPHQWDOWHVWVWRYHULI\9ȡ593GLVWLOODWLRQSURILOHVDQG-)727ǻ3DWºC to ensure that the final blends meet the aviation fuel standards based on these properties.
984
S. Kalakul et al.
3.2. Microcapsules for controlled release of a pesticide design A controlled release device is used to deliver pesticides to crops in order to optimize the delivery of a pesticide and reduce hazards to humans and the environment. The device consists of a pesticide (permethrin) as an active ingredient (AI) that is encapsulated within a polymeric membrane (poly butyl-methacrylate), which controls the amount of the AI to be diffused out into water (the most common release medium in the agrochemical field). In this example, the device template is used to find a donor medium that is able to achieve the target behavior. The target product performance is set at 90 % release of the permethrin to water in 3 hours. The calculation of the needed properties such as diffusivity coefficient between AI and polymer, critical volume, molecular weight and glass temperature of polymer are performed via the property toolbox. The results from the calculation of the controlled release model using different donor mediums indicate that it is possible to achieve 95% release from the capsule in 3 hours by using n-hexane as the donor medium.
4. Conclusion A computer-aided framework for design of chemical products has been developed and used as the architecture for the ProCAPD software. The model libraries, the structured databases and the generic workflow are integrated through the product design ontology developed to represent the associated knowledge. The use of the framework has been highlighted through 2 representative case studies involving tailor-made jet-fuels and microcapsules for controlled release of a pesticide. It helps to reduce the search space and provides promising chemical candidates that are competitive as well as economic and environmentally feasible, satisfying product performance specifications and making it more flexible and capable of solving a wide range of product design problems.
References E. Conte, R. Gani, T.I. Malik, 2011, The virtual Product-Process Design laboratory to manage the complexity in the verification of formulated products, Fluid Phase Equilibria, 302, 294-304. R. Gani, 2004, Chemical Product Design: Challenges & Opportunities, Computers and Chemical Engineering, 28, 2441-2457. R. Gani, K. M. Ng, 2015, Product design – Molecules, devices, functional products, and formulated products, Computers & Chemical Engineering, 81, 70-79. P. M. Harper, R. Gani, 2000, A multi-step and multi-level approach for computer aided molecular design, Computers & Chemical Engineering, 24, 677-683. M. Hill, 2009. Chemical Product Engineering – The third paradigm, Computer & Chemical Engineering, 33(5), 947-953. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 – 2005, 2007, EPA430-R-07-002, U.S. Environmental Protection Agency, Washington, United States of America. S. Kalakul, S. Cignitti, L. Zhang, R. Gani, 2017, Chapter 3 – VPPD-Lab: The Chemical Product Simulator, Tools For Chemical Product Design, Computer Aided Chemical Engineering, 39, 61-94. L. Zhang, D. K. Babi, R. Gani, 2016, New Vistas in Chemical Product and Process Design, Annual Review of Chemical and Biomolecular Engineering, 7, 557-582. L. Zhang, S. Cignitti, R. Gani, 2015, Generic mathematical programming formulation and solution for computer-aided molecular design, Computers & Chemical Engineering, 78, 79-84.