Rafiqul Gani
7004856376
Publications - 14
Multiscale high-throughput screening of ionic liquid solvents for mixed-refrigerant separation
Publication Name: Computers and Chemical Engineering
Publication Date: 2025-08-01
Volume: 199
Issue: Unknown
Page Range: Unknown
Description:
Commonly used mixed-refrigerants are azeotropic mixtures of hydrofluorocarbons (HFCs) with high global warming potential. There is a need for reclamation and recovery of these HFCs. Solvent-based extractive distillation is a promising separation technique for recycling of these refrigerant components. Ionic liquids are suitable solvents for this application due to their negligible vapor pressures, tunable properties, and near-zero waste in closed-loop operations. However, the numerous potential combinations of cation-anion pairs make the selection of the optimal ionic liquid challenging. Moreover, the choice of ionic liquid critically affects energy efficiency and separation performance. To address this challenge, we present a hierarchical, multiscale computational workflow for computer-aided molecular and process design (CAMPD) that combines aspects of molecular simulation, machine learning, process performance measures, and equation-oriented process optimization for the solvent-based separation of azeotropic refrigerant mixtures. We employ a decomposition-based solution approach for CAMPD, where we first perform computer-aided molecular design (CAMD) to identify promising ionic liquid candidates through high-throughput screening, considering 16,352 known and generated ionic liquids. Next, we perform a focused CAMPD to identify the solvents that give the best process performance. We highlight the application of our method for the separation of refrigerants R-32 from R-125, which belong to the binary azeotropic refrigerant mixture commonly known and used as R-410A. Our method identified 43 ionic liquids (24 known and 19 generated) that matched all solvent and separation process specifications. Among these, five ionic liquids are found to be more sustainable and superior to others.
Open Access: Yes
Models, modeling and model-based systems in the era of computers, machine learning and AI
Publication Name: Computers and Chemical Engineering
Publication Date: 2025-03-01
Volume: 194
Issue: Unknown
Page Range: Unknown
Description:
Models, representing a system under study with respect to problems such as process design, process control, product synthesis and many more, are at the core of most computer-aided solution techniques. The representation of a system through a model is done in different ways, such as, symbols, data, mathematical equations, and/or some combination of these. The workflow or process of creating a proxy mathematical representation (model) of a given target system is referred to as modeling. Model-based software tools incorporate the developed models within the steps of their systematic workflow through simultaneous or decomposed solution strategies related to synthesis, design, analysis, etc., of specific systems. In this perspective paper we highlight the various ways systems can be represented by models, the different ways the required models are developed through modeling techniques, and examples of model-based software tools developed to solve different process and product engineering problems. Two types of systems - process systems and chemical systems, are considered. Important issues and challenges are highlighted and perspectives on how they can be addressed are presented.
Open Access: Yes
Innovation through intelligent computer-aided formulation design
Publication Name: Current Opinion in Chemical Engineering
Publication Date: 2025-03-01
Volume: 47
Issue: Unknown
Page Range: Unknown
Description:
This perspective paper presents a focused review of a selected topic of chemical-based products, namely, formulations. As formulations cover a wide range of chemical-based products, we highlight opportunities for innovation in three types of formulations — liquid blends, which are mixtures of chemicals that are in the liquid state at standard conditions; liquid formulations, which are mixtures of chemicals that may exist in different states but the final product is a single-phase liquid; and emulsions, which are also mixtures of chemicals that may exist in different states, but the final product is in the form of an emulsion. In each case, we discuss aspects of design, analysis, and innovation together with issues and challenges that could be tackled to find better and more sustainable products. In particular, the potential of hybrid artificial intelligence augmented computer-aided techniques that can aid in the design, analysis, and innovation of formulations is highlighted.
Open Access: Yes
Computer aided formulation design based on molecular dynamics simulation: Detergents with fragrance
Publication Name: Computers and Chemical Engineering
Publication Date: 2025-01-01
Volume: 192
Issue: Unknown
Page Range: Unknown
Description:
Computer-aided formulation design is a methodology that utilizes domain knowledge and selected methods and tools suitable for computer-based applications to assist in formulation (product) design. In this paper, molecular dynamics simulation and Bayesian neural network algorithms are combined with well-known engineering models to help accelerate the development and optimization of formulation-based detergent products with a view to improve product quality and performance. In particular, the mechanism of the behavior of polymers (an active ingredient in the product) to improve the product quality in terms of the fragrance and its residence time is highlighted. Results from molecular dynamic simulation applied to study the molecular interaction mechanism show that the polymers have an attraction effect with fragrance molecules and could adsorb more to make them to stay on the surface of clothes. In addition, the polymer attenuates the diffusion of the fragrance molecules, lengthening the entire process of fragrance diffusion, which is the essence of the ability of the polymer to slow down the release of the fragrance. A Quantitative Structure-Property Relationship (QSPR) model between component proportions and fragrance diffusion is established through Bayesian Neural Network (BNN) and the product formulation is optimized based on this model. Keeping polymer and perfume ingredients unchanged, the surfactant amounts are optimized to provide improved product quality.
Open Access: Yes
A Perspective on Artificial Intelligence for Process Manufacturing
Publication Name: Engineering
Publication Date: 2025-09-01
Volume: 52
Issue: Unknown
Page Range: 60-67
Description:
To achieve sustainable development goals and the requirements of a circular economy, a new class of intelligent computer-aided methods and tools is needed. Artificial intelligence (AI) techniques have been gaining much attention due to their ability to provide options to tackle the challenges we are currently facing. However, the successful application of AI to solve problems of current interest requires AI to be integrated with associated process systems engineering methods and tools that are already available or being developed. In this perspective paper, we highlight the use of a collection of process systems engineering methods and tools augmented by AI techniques to solve problems related to process manufacturing, with a focus on chemical product design, process synthesis and design, process control, and process safety and hazards.
Open Access: Yes
Assessing the future impact of 12 direct air capture technologies
Publication Name: Chemical Engineering Science
Publication Date: 2024-10-05
Volume: 298
Issue: Unknown
Page Range: Unknown
Description:
Direct Air Capture (DAC) is regarded as an effective method to decrease the concentration of CO2 in the atmosphere and thus alleviate the greenhouse effect. This article conducts a comparative analysis of the CO2 emissions of 12 state-of-the-art DAC technologies. The evaluations consider regional (EU, USA, and China) and temporal (years 2023, 2030, and 2050) energy supply variations. It is found that the CO2 emissions generally decrease over time for all the different regions considered. The best CO2 emission performance is found in Europe, followed by the United States and China. The evaluation also finds that currently a substantial number of DAC technologies could not achieve net-negative emission, especially for China. In 2050, most of the DAC technologies are found to perform significantly better in terms of their negative emission performance. We also found that the utilization of fossil fuels, especially coal, needed to operate the DAC process, substantially hinders its ability to achieve net-negative emission. Electrochemical-based technologies are found to outperform others in all scenarios, especially when powered with renewable electricity. The DAC technologies relying on steam-based sorbent regeneration can greatly reduce their CO2 emission when low-carbon energy is used for steam generation. Finally, in all the different scenarios, the DAC technologies incorporating high-temperature calcination regenerations exhibit the worst performance due to the lack of low-emission energies for generating fired heat.
Open Access: Yes
An Improved Machine Learning Model for Pure Component Property Estimation
Publication Name: Engineering
Publication Date: 2024-08-01
Volume: 39
Issue: Unknown
Page Range: 61-73
Description:
Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product design. However, the lack of extensive data and high experimental costs hinder the development of prediction techniques for these properties. Moreover, accuracy and predictive capabilities still limit the scope and applicability of most property estimation methods. This paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach. A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different compounds. Prior selection techniques, including prior elicitation and prior predictive checking, are also applied during the building procedure to provide the model with more information from previous research findings. The framework is assessed using datasets of varying sizes for 20 pure component properties. For 18 out of the 20 pure component properties, the new models are found to give improved accuracy and predictive power in comparison with other published models, with and without machine learning.
Open Access: Yes
A virtual screening framework based on the binding site selectivity for small molecule drug discovery
Publication Name: Computers and Chemical Engineering
Publication Date: 2024-05-01
Volume: 184
Issue: Unknown
Page Range: Unknown
Description:
Structure-based virtual screening of binding of candidate drug molecules is a topic of increasing interest in the discovery of small molecule drugs. As the same drug molecule may bind to different binding sites on a target protein, the binding site selectivity that is related to the binding tendency of candidate drug molecules to different binding sites after reaching the target protein need to be considered in sufficient details. In this work, a systematic and computer-aided virtual screening framework based on the binding site selectivity to screen candidate drug molecules in terms of their ability to bind on selected sites is presented. The framework integrates two machine learning (ML)-based models to predict the binding potential and binding selectivity to specific binding sites that are important for virtual screening of drug molecules. The details of the ML-based models together with the work-flow of the computer-aided virtual screening methods and the efficient and consistent integration of related drug design tools are presented. The applicability of this virtual screening framework is illustrated through a case study involving the screening for drug molecules as inhibitors to block the binding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to angiotensin converting enzyme 2 (ACE2), which is the target protein. The case study results point to identification of new candidate inhibitors with better binding site selectivity than two known potential inhibitors, Nilotinib and SSAA09E2.
Open Access: Yes
Ionic liquid binary mixtures: Machine learning-assisted modeling, solvent tailoring, process design, and optimization
Publication Name: Aiche Journal
Publication Date: 2024-05-01
Volume: 70
Issue: 5
Page Range: Unknown
Description:
This work conducts a comprehensive modeling study on the viscosity, density, heat capacity, and surface tension of ionic liquid (IL)-IL binary mixtures by combining the group contribution (GC) method with three machine learning algorithms: artificial neural network, XGBoost, and LightGBM. A large number of experimental data from reliable open sources is exhaustively collected to train, validate, and test the proposed ML-based GC models. Furthermore, the Shapley Additive Explanations technique is employed to quantify the influential factors behind all the studied properties. Finally, these ML-based GC models are sequentially integrated into computer-aided mixed solvent design, process design, and optimization through an industrial case study of recovering hydrogen from raw coke oven gas. Optimization results demonstrate their high computational efficiency and integrability in solvent and process design, while also highlighting the significant potential of IL-IL binary mixtures in practical applications.
Open Access: Yes
DOI: 10.1002/aic.18392
eSFILES: Intelligent process flowsheet synthesis using process knowledge, symbolic AI, and machine learning
Publication Name: Computers and Chemical Engineering
Publication Date: 2024-02-01
Volume: 181
Issue: Unknown
Page Range: Unknown
Description:
Process flowsheet synthesis, design, and simulation require integrated approaches that combine domain knowledge and data-driven methods for fast, efficient, and reliable solutions. However, due to the recent surge in data and machine learning capabilities, there has been a shift towards building purely data-driven systems for process flowsheet synthesis and related problems. Such approaches have certain drawbacks. Here, we present a hybrid method that combines data-driven approaches with domain knowledge to represent process flowsheets and solve problems related to process synthesis, design, and simulation. We present an extended SFILES (or eSFILES) representation, a multi-level hierarchical flowsheet representation with varying degrees of process knowledge. At level 0, flow diagrams are represented as purely text-based SFILES strings. At level 1, the SFILES grammar, along with inferencing algorithms, is used to construct a flowsheet hypergraph explicitly representing flow diagram connectivity. At level 2, specifications needed for material and energy balance calculations are introduced, and, after simulation, the results are also added using annotated flowsheet hypergraphs. Finally, at level 3, a process ontology is connected with the annotated flowsheet hypergraph to include design and operation parameters as well as the detailed simulation results. We discuss this hierarchical framework using several case studies.
Open Access: Yes
Computer-aided Molecular and Process Design (CAMPD) for Ionic Liquid Assisted Extractive Distillation of Refrigerant Mixtures
Publication Name: Computer Aided Chemical Engineering
Publication Date: 2024-01-01
Volume: 53
Issue: Unknown
Page Range: 1303-1308
Description:
Computer-aided Molecular and Process Design, CAMPD, is a technique that simultaneously optimizes the choice of materials, such as solvents, and the corresponding process configurations for many chemical separation processes. The technique involves formulating an equation-oriented optimization model representing the overall design problem, which then can be solved in many ways depending on the chemicals involved, the property and process models, and the complexity and size of the problem, among others. Due to the complexity and large-size of the problem, and a lack of predictive property models, we have applied a decomposition-based CAMPD strategy that involves solving a series of subproblems sequentially to reduce the overall search space, thereby reducing the computational burden. We illustrate our strategy through a case study involving the design of ionic liquids (ILs) as solvents for the extractive-distillation based separation of an azeotropic refrigerant mixture, R-410A. Separation of such mixtures is gaining increased interest due to the need to remove, substitute or reuse constituent refrigerant chemicals that have undesirable properties (such as high global warming potential, flammability, etc.). ILs are considered because of their designable properties as functions of their molecular structures. Based on available measured data, group-contribution based predictive property models have been developed and interfaced with the workflow of the proposed strategy. A set of promising ILs have been identified and their performance verified through process simulation.
Open Access: Yes
Hybrid Artificial Intelligence-based Process Flowsheet Synthesis and Design using Extended SFILES Representation
Publication Name: Computer Aided Chemical Engineering
Publication Date: 2024-01-01
Volume: 53
Issue: Unknown
Page Range: 1279-1284
Description:
Process flowsheet synthesis and design involves simultaneously solving several problems, including determining the unit operations and their sequence, underlying reactions and reaction stoichiometry, downstream separation design and operation parameters, sustainability factors, and many more. Naturally, this results in a large amount of data being associated with a given process flowsheet that captures the relevant process context and should be readily accessible. This data is useful for solving related problems both using data-driven and process knowledge-based methods. A hierarchical framework, called the extended SFILES (or eSFILES), proposed recently stores this information using a combination of text-based, graph-based, and ontology-based representations. Here, we provide details on a prototype software for automated flowsheet representation and generation across various levels in the eSFILES framework. The underlying methods include a novel flowsheet grammar, a set of inferencing algorithms, and interfacing with a commercial process simulator facilitating rigorous flowsheet simulation.
Open Access: Yes
A novel hybrid process design for efficient recovery of hydrophilic ionic liquids from dilute aqueous solutions
Publication Name: Aiche Journal
Publication Date: 2023-11-01
Volume: 69
Issue: 11
Page Range: Unknown
Description:
Ionic liquids (ILs) have received much attention in both academia and industries due to their superior performance in many applications. Efficient recovery/recycling of ILs from their dilute aqueous solutions is essential for the acceptance and implementation of many IL-based technologies by industry. In this work, a practical and cost-effective hybrid process design method that combines aqueous two-phase extraction, membrane separation, and distillation operating at their highest efficiencies is proposed for the recovery of hydrophilic ILs from dilute aqueous solutions. The application of this hybrid process design method is illustrated through case studies of recovering two hydrophilic ILs, n-butylpyridinium trifluoromethanesulfonate ([C4 Py][TfO]) (CAS number: 390423-43-5) and 1-butyl-3-methylimidazolium chloride ([C4 mIm][Cl]) (CAS number: 79917-90-1), from their dilute aqueous solutions. For the recovery of 10 wt.% [C4 Py][TfO] from aqueous solution, the hybrid process using (NH4 )2 SO4 as the salting-out agent could reduce the total annual cost (TAC) and energy consumption by 57% and 91%, respectively, compared with the pure distillation processes. In the case of recovering 10 wt.% [C4 mIm][Cl] from aqueous solution, the reduction in TAC and energy savings of the hybrid process with salting-out agent (NH4 )2 SO3 could reach 49% and 87%, respectively, compared with the pure distillation process. Furthermore, uncertainty analysis through Monte Carlo simulations show that the proposed hybrid process design is more robust to uncertainties in energy prices and other material (e.g., equipment and solvent) costs.
Open Access: Yes
DOI: 10.1002/aic.18198
A systematic design of integrated palm-oil biorefinery networks: Identifying sustainable solutions
Publication Name: Sustainable Production and Consumption
Publication Date: 2023-11-01
Volume: 42
Issue: Unknown
Page Range: 138-157
Description:
This paper presents a comprehensive and systematic analysis of synthesis and design of sustainable palm oil integrated biorefinery networks involving multiple platforms of bioresources for sharing materials, energy, and facilities to obtain a more sustainable solution. There are three main processing platforms consisting of palm oil, palm biomass, and biogas from palm oil mill effluent (POME-biogas). The alternatives are generated from a superstructure of each platform representing different products that can be made and their established processing routes. Utilizations of glycerol, bio-syngas, and CO2 , which are by-products of the palm oil, palm biomass, and POME-biogas platforms, respectively, are also considered as three other platforms. Different scenarios of materials, energy, and facility integration among the platforms are analyzed by considering economic benefits together with CO2 emissions, as well as Life Cycle Assessment (LCA), which includes climate change impact and other environmental impact categories. Analyses of the design of the palm oil integrated biorefinery network with multiple process networks and heat integration point to the achievement of more sustainable solutions for production and consumption compared to the scenario of business as usual (BAU). The sustainable palm oil integrated biorefinery network corresponds to economic improvement and CO2 reduction potential as well as satisfying environmental impacts. The analysis results show that the palm oil integrated biorefinery network alternatives can provide high economic potential and less environmental impacts compared to without any integration. The best non-tradeoff solution proposes the integration of palm biomass and POME-biogas platforms for bio-methanol production and the integration of palm oil platform with glycerol production to produce 1,2-propanediol as an additional product. It offers 23.5 million dollars per year of economic value-added benefit with 2.9 years of payback period while also reducing the environmental impacts. Wind power and river water are selected to maximize profitability options for electricity and freshwater supplies, respectively.
Open Access: Yes
