Chemical Process Design Research Articles

GREEN CHEMISTRY EDUCATION

Green chemistry can also be referred to as sustainable chemistry and it is the design of chemical products and processes aimed at less or less the use of hazardous substances. It's about lessening the destructive consequences on the environment and the earth's sustainability (Wale et al., 2023; Mane et al., 2023). This accommodates many principles that outline how to design safer chemical reactions as well as technology and the use of green chemicals (De, 2023; Rathi et al., 2023). Such principles include the elimination or reduction of generation, using renewable raw materials, and the production of safer substances and materials to decrease harm to human health and the environment, according to Nithya and Sathish (2023). Thus, green chemistry's goal is to bring radical changes in industries researching for effective and eco-friendly strategies for the synthesis of materials, including nanomaterials, through employing cost-efficiency and biocompatibility with the help of earth's resources (De, 2023).

A Leap Forward in Chemical Process Design: Introducing an Automated Framework for Integrated AI and CFD Simulations

Despite the numerous possibilities of integrating AI and CFD simulations for chemical process design, researchers often rely on manual techniques, resulting in suboptimal models and time-consuming processes. To address these challenges, we propose an automated framework that combines high-fidelity AI modeling with hyperparameter optimization, automated CFD simulations using OpenFOAM, and effortless post-processing for data extraction. This framework was tested on a reactor scale-up process involving 51 different configurations for butadiene synthesis, achieving 98.8% accuracy in CFD validation and over 99% accuracy in AI models. The automation pipeline streamlines geometry generation, meshing, simulation, data extraction, and AI-driven optimization, significantly reducing manual effort. Our framework is versatile, customizable for various types of process equipment design, and employs open-source software for ease of adoption and reproducibility. This approach not only enhances accuracy and efficiency but also opens up AI and CFD integration to a broader range of researchers.

Preface

2024 4th International Conference on Fluid and Chemical Engineering (ICFCE 2024) has been successfully held on July 26-27, 2024, in Wuhan China as a virtual event. Organized by Hubei Zhongke Institute of Geology and Environment Technology, ICFCE 2024 intends to invite worldwide famous scientists, experts, scholars, and researchers for academic presentations. ICFCE 2024 proceeding is a collection of outstanding submissions in the field of biofluid mechanics, chemically reacting fluids and combustion, computational fluid dynamics (CFD), experimental fluid mechanics, flow through porous media, fluid–solid interactions (FSI), geophysical fluid dynamics, granular/suspension flows, heat and mass transfer, hydrodynamics, physical, theoretical and computational chemistry, chemical engineering fundamentals, chemical reaction engineering, chemical engineering equipment design and process design, thermodynamics, catalysis & reaction engineering, particulate systems, crystallization, etc. Around 65 articles were selected from 146 submissions after peer-review, and authors and guests participated in ICFCE 2024 with keynote speeches, oral presentations and posters, promoting the communication among researchers and experts from institutes and universities, including University of Sharjah, The University of Manchester, Harbin Engineering University, China University of Petroleum (East China), Shanghai Maritime University, Jiangxi University of Science and Technology, Nagoya University, Marine Design & Research Institute of China, Tianjin Transportation Technical College, Okayama University, etc. Cordial gratitude is delivered to Technical Program Committee of ICFCE 2024 for the professional work on guarantying the high standard of conference as well as the proceedings. We believe the proceedings will enhance the exchange of ideas and state-of-art knowledge on Fluid and Chemical Engineering. Finally, we thank all the staff members at IOP Publishing for their valuable editorial support. Professor Omid Mahian Xian Jiaotong University, China Associate Professor Sébastien Déon University of Bourgogne-Franche-Comté, France List of Technical Committee is available in the pdf

Effective lateral dispersion of momentum, heat and mass in bubbling fluidized beds

The lateral dispersion of bed material in a bubbling fluidized bed is a key parameter in the prediction of the effective in-bed heat transfer and transport of heterogenous reactants, properties important for the successful design and scale-up of thermal and/or chemical processes. Computational fluid dynamics simulations offer means to investigate such beds in silico and derive effective parameters for reduced-order models. In this work, we use the Eulerian-Eulerian two-fluid model with the kinetic theory of granular flow to perform numerical simulations of solids mixing and heat transfer in bubbling fluidized beds. We extract the lateral solids dispersion coefficient using four different methods: by fitting the transient response of the bed to (1) an ideal heat or (2) mass transfer problem, (3) by extracting the time-averaged heat transfer behavior and (4) through a momentum transfer approach in an analogy with single-phase turbulence. The method (2) fitting against a mass transfer problem is found to produce robust results at a reasonable computational cost when assessed against experiments. Furthermore, the gas inlet boundary condition is shown to have a significant effect on the prediction, indicating a need to account for nozzle characteristics when simulating industrial cases.

Assembly Regulates Gamma Radiation Polymerization of Polytelluoxane.

Regulating chemical drug's responsiveness to gamma radiation is crucial for achieving better therapeutic effects in cancer treatment. Most research focused on thermodynamic chemical structure design, while little attention was paid to kinetic regulate strategy, which possesses greater universality and security. In this study, we achieved a kinetic-based regulate strategy of gamma radiation reaction, through the construction of microphase environment during polymerization of polytelluoxane (PTeO). We designed hydrophobic segments forming large compound micelles (LCMs) assembly to create kinetically favorable higher concentration for radiation-induced reaction. It exhibited a > ten times higher responsiveness and, as far as we know, merely required a minimum dosage of 5 Gy for polymerization to occur. What's more, by taking advantages of the assembly change with Te-O hydrophilic segments and gamma radiation, polymerization became milder with lower polydispersity than previous methods. Such kinetic-based regulate strategy could offer a novel perspective on the design of radiation-responsive chemoradiotherapy and other radiation-induced chemical process.

Enhancing 3DSPs estimation: The dispersive solubility parameter adjusted by means of molar refraction, coordination number and molar volume using the DiPEVa model

This study comprehensively analyzes the empirical relationships between polarizability measurements, such as refractivity index and molar refraction, or the length of the homologous chain (HCL), and the dispersive solubility parameter (δD). The authors investigated the correlation between these parameters by utilizing a database encompassing various chemical groups across a range of aliphatic and aromatic molecules. An innovative approache enhaces the well known relationship between the the dispersive solubility parameter (δD) and the Lorentz-Lorenz function, adjusting the dispersive solubility parameters by the ratio of coordination numbers and molar volumes as correction factors. Notably, the adjusted δD demonstrated a strong linear correlation with MR, suggesting that MR can be a reliable predictor for δD. This was further substantiated by the strong correlation coefficients obtained (such as an adjusted R-Square of 0.866), indicating the robustness of our empirical models. In conclusion, our research provides new insights into the solubility behaviour of molecules with the derived empirical relationships offering a valuable tool for predicting solubility parameters. These findings have practical implications for solvent selection, understanding liquid behaviours, and the design of chemical processes and materials.

Estimation of the interaction parameters between carbon dioxide and an organic solvent by the Peng–Robinson equation of state via an artificial neural network

The equation of state (EoS) is a tool for estimating the thermodynamic and physical properties of compounds, including mixtures, across a range of temperatures and pressures. When dealing with mixtures, a mixing rule is required to calculate the mixture parameters. Mixing rules may involve interaction parameters, such as kij and lij, that correct for differences between components. However, obtaining this data requires specialized equipment and techniques and significant measurement time, resulting in limited reported EoS parameters. In this study, we introduce an artificial neural network (ANN) to predict interaction parameters in the van der Waals one-fluid mixing rule. These parameters are used to calculate the physical properties of mixtures using the Peng–Robinson (PR) EoS. The interaction parameters are used in two cases, namely the one-parameter and two-parameter mixing rules (OP and TP, respectively), in which only kij and both kij and lij are employed, respectively. The vapor–liquid equilibrium (VLE) data of CO2/organic solvent binary systems are collected and correlated by the PR EoS to construct a database of 1286 and 1292 parameters for the OP and TP, respectively. The molecular weight, critical temperature and pressure, acentric factor of the organic solvent, and temperature are used as input parameters for the ANN. In addition, we optimize the structure of the ANN by changing the activation function, number of neurons, and number of hidden layers. The optimized ANN uses a tanh activation function. Hidden layers are used for both the OP and TP, along with 40 and 50 neurons, respectively. The results confirm that the model can determine the interaction parameters of the PR EoS, which can be used to estimate the VLE. These results are useful for incorporation into process simulators for chemical process design.

A novel multi-objective optimization strategy based on vibrating particle system algorithm applied to chemical process design

Chemical process design is a highly interesting field within the chemical engineering community due to the numerous potential applications it offers. Traditionally, this problem involves multiple objectives and constraints related to mass, energy, and momentum balances, as well as limitations based on environmental, physical, and operating conditions. Over the past few decades, various multi-objective optimization strategies incorporating evolutionary algorithms have been proposed and tested using test cases of varying complexity. In this study, we propose a new Multi-objective Optimization Vibrating Particle System (MOVPS) algorithm. This numerical strategy extends the Vibrating Particle System Algorithm to a multi-objective context by incorporating three operators into the original algorithm: Pareto dominance, crowding distance, and a mutation strategy. To expedite the convergence process and avoid local optima, an external repository is considered. We evaluate the performance of the proposed methodology by applying it to mathematical functions (ZDT functions) and chemical process design problems. Regarding the ZDT functions, the computational cost (number of objective function evaluations and the processing time) was not increased considering the proposed methodology, as well as the values of convergence and diversity metrics are in agreement with those calculated considering other multi-objective optimization strategies. For most engineering case studies, it was possible to verify that the proposed methodology was able to obtain good estimates for the Pareto curve without increasing the computational cost. However, for the industrial styrene reactor problem it was possible to improve the Pareto curve and reduce the computational cost in relation to other multi-objective optimization algorithms.

Visible Light-promoted C(sp3)-H α-Carbamoylation of Cyclic Ethers with Isocyanides.

A protocol exploiting isocyanides as carbamoylating agents for the α-C(sp3)-H functionalization of cyclic ethers has been optimized via a combined visible light-driven hydrogen atom transfer/Lewis acid-catalyzed approach. The isocyanide substrate scope revealed an exquisite functional group compatibility (18 examples, with yields up to 99 %). Both radical and polar trapping, kinetic isotopic effect and real-time NMR studies support the mechanistic hypothesis and provide insightful details for the design of new chemical processes involving the generation of oxocarbenium ions.

Photocatalytic degradation properties

Photocatalysis, an innovative technology, holds promise for addressing industrial pollution issues across aqueous solutions, surfaces, and gaseous effluents. The efficiency of photodegradation is notably influenced by light intensity and duration, underscoring the importance of optimizing these parameters. Furthermore, temperature and pH have a significant impact on pollutant speciation, surface chemistry, and reaction kinetics; therefore, process optimization must consider these factors. Photocatalytic degradation is an effective method for treating water in environmental remediation, providing a flexible and eco-friendly way to eliminate organic contaminants from wastewater. Selectivity in photocatalytic degradation is achieved by a multidisciplinary approach that includes reaction optimization, catalyst design, and profound awareness of chemical processes. To create efficient and environmentally responsible methods for pollution removal and environmental remediation, researchers are working to improve these components.

Synthesis of Triphenyl Imidazole by Green chemistry approach

Synthesis of 2,3,5- Triphenyl imidazole is carried out by conventional method/ traditional method as well as green chemistry in the present study. Green chemistry is also known as micro-wave assisted synthesis. Green chemistry is the design of chemical products and process that eliminates the use and generation of hazardous substances. Using green solvent, like water, synthesis of biologically active moiety with high percentage yield as well as purity. The Percentage yield of 2,3,5- Triphenyl imidazole obtained by Green Chemistry Approach is 90.90% whereas Conventional/Traditional method produces only 69.60% of 2,3,5- Triphenyl imidazole constant concentration of all organic reagents. Hence, we concluded that green chemistry approach is environment friendly.

An optimization-free Fisher information driven approach for online design of experiments

Developing mathematical models used to elucidate reaction kinetics plays a crucial role in the design, control, and optimization of chemical processes. One of the most challenging tasks in kinetic model identification is the precise estimation of unknown kinetic model parameters. This challenge can be effectively addressed through the application of Model-Based Design of Experiments (MBDoE) techniques, which enable the design of experiments facilitating precise model parameter estimation with minimal runs and analytical resources. Nevertheless, MBDoE techniques rely on an optimization procedure that is susceptible to parametric uncertainty, making the design procedure computationally intensive and prone to issues of local optimality. MBDoE techniques are also employed in online procedures to expedite the identification of kinetic models in autonomous platforms. As a result, ensuring rapid convergence becomes imperative to mitigate numerical issues during operational processes. In this paper a Fisher Information Matrix Driven (FIMD) approach is introduced to tackle these challenges. The methodology integrates a sampling-based experimental design approach with experiment ranking based on FIM to select the most informative experiment at each iteration. The effectiveness of the proposed design methodology is examined and discussed via two different case studies of increasing complexity: a fed-batch reactor in which the fermentation of baker’s yeast is carried out and a nucleophilic aromatic substitution in a flow reactor system.

Liquid–Liquid Equilibrium Behavior of Ternary Systems Comprising Biodiesel + Glycerol and Triglyceride + Methanol: Experimental Data and Modeling

Having a comprehensive knowledge of phase equilibrium is advantageous for industrial simulation and design of chemical processes. For further acquisition of primary data to facilitate the separation and purification of waste oil biodiesel systems, a liquid–liquid equilibrium (LLE) tank is deployed for the ternary system of waste oil biodiesel + methanol + glycerin, thereby enhancing the precision and efficiency of the process. The phase equilibrium system was constructed under the influence of atmospheric pressure at precise temperatures of 303.15 K, 313.15 K, and 323.15 K. The equilibrium components of each substance were analyzed by employing high-temperature gas chromatography, a sophisticated analytical method that enables the identification and quantification of individual components of a sample. Moreover, the ternary liquid–liquid equilibrium data were correlated by implementing the NRTL and UNIQUAC activity coefficient models. Subsequently, the binary interaction parameters of the ternary system were derived by conducting regression analysis. The experimental data demonstrated that the presence of lower methanol content in the system resulted in nearly immiscible biodiesel and glycerol phases, which ultimately facilitated the separation of biodiesel and glycerol. Conversely, with the increase in methanol content, the mutual solubility of biodiesel and glycerol was observed to increase gradually. The results showed that the calculated values of the NRTL and UNIQUAC models aligned well with the experimental values. The root-mean-square deviations of the NRTL and UNIQUAC models at 313.15 K were 2.76% and 3.56%, respectively.

Isomerization Engineering of Oxygen-Enriched Carbon Quantum Dots for Efficient Electrochemical Hydrogen Peroxide Production.

Hydrogen peroxide (H2O2) has emerged as a kind of multi-functional green oxidants with extensive industrial utility. Oxidized carbon materials exhibit promises as electrocatalysts in the two-electron (2e-) oxygen reduction reaction (ORR) for H2O2 production. However, the precise identification and fabrication of active sites that selectively yield H2O2 present a serious challenge. Herein, a structural engineering strategy is employed to synthesize oxygen-doped carbon quantum dots (o-CQD) for the 2e- ORR. The surface electronic structure of the o-CQDs is systematically modulated by varying isomerization precursors, thereby demonstrating excellent electrocatalyst performance. Notably, o-CQD-3 emerges as the most promising candidate, showcasing a remarkable H2O2 selectivity of 96.2% (n = 2.07) at 0.68V versus RHE, coupled with a low Tafel diagram of 66.95mV dec-1. In the flow cell configuration, o-CQD-3 achieves a H2O2 productivity of 338.7mmol gcatalyst -1 h-1, maintaining consistent production stability over an impressive 120-hour duration. Utilizing in situ technology and density functional theory calculations, it is unveil that edge sites of o-CQD-3 are facilely functionalized by C-O-C groups under alkaline ORR conditions. This isomerization engineering approach advances the forefront of sustainable catalysis and provides a profound insight into the carbon-based catalyst design for environmental-friendly chemical synthesis processes.

Magnetite nanoparticles (Fe3O4 NPs) performed by the co-precipitation and green synthesis processes

Abstract The design of chemical processes and products with a low use of hazardous substances is known as “green chemistry.” The method of producing nanoparticles (NPs) significantly affects their size and characteristics. Currently, there are two primary methods utilized for synthesizing NPs, known as the bottom-up and top-down approaches. To keep the prices of nanotechnology-based products, the green method employs plant metabolites as reducing agents. Two types of magnetite NPs (Fe3O4 NPs) were synthetized and characterized being denominated NPs1 synthetized by the co-precipitation method and NPs2 synthetized using the green synthesis process. It was found that the Fe3O4 NPs1 has a quasi-spherical morphology with a size of 43 nm, while the Fe3O4 NPs2 has a size of 6 nm.

A Review on Green Chemistry and Its Applications

Green Chemistry is most investigated subject these days. It is also known as sustainable chemistry. Green Chemistry and sustainable chemistry are equals that helps to save and conserve resources. It decreases the effect of chemical products on human and atmospheric health. Green Chemistry gives 12 principles that reduce and prevent the utilization and manufacture of hazardous substance. These principles of green chemistry inform about the various issues related to production of chemical compound. Green Chemistry concept started approximately three decades ago. Green chemistry concept for the chemical synthesis and application launched in 1990. This review article purpose to study about Green chemistry and its application and how green chemistry connect to the phytochemistry. By establishing Green Aspiration Level (GAL) that is a quantifiable descriptor, process greenness becomes measurable. Green house gases and global warming are the problem that affected the human and environment health. Green chemistry is used in daily life mainly in medicines. Tree bark and Pine bark extracts are mostly used in anti-microbial, antioxidant and anticancer drugs. The matter that present in environment gives applicable things to the researchers and engineers for the changing and developing a sustainable culture. The word “Green chemistry” is given by the IUPAC which means: the invention, design and utilization of chemical product and process to reduce or to eliminate the use and production of unsafe substance.

Assessment of the estimation methods for the normal boiling point of liquid organic hydrogen carriers

Liquid organic hydrogen carrier (LOHC) presents a promising solution for hydrogen storage. However, its development has been impeded by the absence of normal boiling point (NBP) data for hydrogenation products, especially saturated alicyclic compounds. This gap spurs the search for a reliable and convenient estimation method to aid in the screening of new mediums and the design of chemical processes. Thanks to the intrinsic simplicity, 17 group contribution models were selected and evaluated to identify the optimal ones for different types of alicyclic compounds. The best-performing model was subsequently refined. Furthermore, two models tailored for predicting the NBP of saturated alicyclic compounds were proposed using classical approaches and artificial neural networks. The results indicate that the GCN and GM models yield lower errors in estimating the NBP of alicyclic hydrocarbons, while the GIC2 model performs better for heterocyclic compounds. The developed models are more accurate compared to the existing models. Notably, the neural-network-based model exhibits excellent performance. These models are expected to provide useful insights for large-scale screening of LOHCs for hydrogen storage.

Analytical and Numerical Thermodynamic Equilibrium Simulations of Steam Methane Reforming: A Comparison Study

Computer simulation is a crucial element in the design of chemical processes. Although numerous commercial software options are widely recognized, the expense associated with acquiring and sustaining valid software licenses can be prohibitive. In contrast, open-source software, being freely available, provides an opportunity for individuals to study, review, and modify simulation models. This accessibility fosters technology transfer and facilitates knowledge dissemination, benefiting both academic and industrial domains. In this study, a thermodynamic equilibrium steady-state analysis of steam methane reforming using a natural-gas-like intake fuel was conducted. An analytical method was developed on the Microsoft Excel platform, utilizing the material balance equations system. The obtained results were compared to numerical methods employing the free-of-charge chemical process simulation software COCO and DWSIM. The investigation explored the influence of temperature, pressure, and steam-to-carbon ratio to determine optimal operating conditions. The findings suggest that higher temperatures and lower pressures are highly favorable for this process, considering that the choice of steam-to-carbon ratio depends on the desired conversion, with a potential disadvantage of coke formation at lower values. Consistent results were obtained through both analytical and numerical methods. Notably, simulations performed using DWSIM showed a deviation of 6.42% on average compared to COCO values. However, it was observed that the analytical method tended to overestimate the results by an average of 3.01% when compared to the simulated results from COCO, highlighting the limitations of this analytical approach.

Group Contribution Revisited: The Enthalpy of Formation of Organic Compounds with “Chemical Accuracy” Part V

Group Contribution (GC) methods to predict thermochemical properties are eminently important in chemical process design. Following our earlier work in which a Group Contribution (GC) model was presented to account for the gas-phase heat of formation of organic molecules which, for the first time, revealed chemical accuracy (1 kcal/mol or 4 kJ/mol), we here present Group Contribution parameters for a range of additional series of molecules allowing the application to a wider range of molecules whilst, mostly, retaining chemical accuracy. The new classes of molecules include amines, alkylesters, and various substituted benzenes, including t-butyl-benzenes, phenols, methoxybenzenes, anilines, benzaldehydes, and acetophenones, and finally furans and indoles/indolines. As in our previous works on this theme, again the critical selection of experimental data was crucial. Not meeting the criterion for chemical accuracy occurred when steric interactions such as nearest neighbour substituents on a benzene ring were present, something which does not fit with the characteristics of the Group Contribution method. We also report some cases for which the experimental value does not seem correct, but where both the G4 and GC model values agree well. In general, in line with accounts in the literature, the G4 method performs really well. Contrary to other related works, we have applied conformational averaging to obtain a slightly more realistic G4 result. Although the difference is generally only a few kJ/mol, this may still be relevant when attempting the development of a model with chemical accuracy, e.g., 4.2 kJ/mol.

Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates.

Fast and accurate prediction of solvent effects on reaction rates are crucial for kinetic modeling, chemical process design, and high-throughput solvent screening. Despite the recent advance in machine learning, a scarcity of reliable data has hindered the development of predictive models that are generalizable for diverse reactions and solvents. In this work, we generate a large set of data with the COSMO-RS method for over 28 000 neutral reactions and 295 solvents and train a machine learning model to predict the solvation free energy and solvation enthalpy of activation (ΔΔG‡solv, ΔΔH‡solv) for a solution phase reaction. On unseen reactions, the model achieves mean absolute errors of 0.71 and 1.03 kcal mol-1 for ΔΔG‡solv and ΔΔH‡solv, respectively, relative to the COSMO-RS calculations. The model also provides reliable predictions of relative rate constants within a factor of 4 when tested on experimental data. The presented model can provide nearly instantaneous predictions of kinetic solvent effects or relative rate constants for a broad range of neutral closed-shell or free radical reactions and solvents only based on atom-mapped reaction SMILES and solvent SMILES strings.