Modeling Of Chemical Processes Research Articles

The Influence of Selected Parameters of the Mathematical Model on the Simulation Performance of a Municipal Waste-to-Energy Plant Absorber

The primary research aim of this manuscript was to present a simplified absorber model and analyse the simulation results of the absorber model created to which, by design, only water was added and the outlet flue gas temperature was optimal. The obtained simulation results of the simplified absorber model were appropriately compared with the operational results of absorbers operating in professional WtE installations. This study focused on the simulation duration. The primary tool used in the paper is OpenFOAM (v2112). Two solvers were used for the calculations: ReactingParcelFoam and LTSReactingParcelFoam. They ran numerical tests on simplified absorber models. We evaluated the results according to the simulation time. We also examined the difference between the measured and calculated flue gas outlet temperatures. The results will guide further research on the absorber. They will speed up and improve the modelling of chemical processes. The only challenge was to define the chemical reactions and add a calcium molecule to the water droplet model. This work shows that we can simplify the absorber’s geometric model. It kept a low relative error and cuts the compute time. Using a local time step instead of a global one in numerical calculations significantly reduced their run time. It did this without increasing the relative error. The research can help develop complex three-phase flow models in the absorber in the future.

Использование компьютерной программы «ПРОЕКТ ВПУ» для проведения технологического расчета и анализа технологий обессоливания воды на ТЭС и АЭС

There are many viable options of desalination schemes that allow us to obtain purified water of the required quality. To conduct process design of a water treatment plant, thermal power plants and nuclear power plants use special software provided by manufacturers of ion exchangers and membrane elements. However, these software programs consider individual water treatment technologies and are attached to filter and membrane materials of a specific manufacturer. They do not provide the ability to calculate complete water desalination schemes, as well as determine the performance indicators of the water treatment plant. It is relevant to develop a software program that allows us to conduct process design of complete water treatment schemes, which include various water treatment technologies, and to determine technological, environmental, and technical-economic indicators. Multivariate calculations with different initial data will allow us to identify the advantages and disadvantages of scheme solutions and determine areas of their rational use. The method of mathematical modeling of chemical and technological processes of water treatment at thermal power plants and nuclear power plants has been used. An application software program “PROJECT WTP” has been developed. It is designed for process design of various water treatment schemes, which are visually assembled from individual elements, and allows one to determine the technological, environmental, and performance indicators of the operation of water treatment plant. Computation data on the auxiliary need coefficient has been obtained for the main water desalination schemes and under condition of different mineralization of the source water. The developed software program can be used for process design of various water treatment schemes and assessment of the degree of improvement of water desalination technology.

An inductive transfer regression framework for small sample modeling in power plants

Small sample size presents a significant challenge in process modeling, making machine learning (ML) models prone to overfitting and reduced accuracy. To address this issue, this study develops a novel inductive transfer regression framework called double-weight least squares support vector regression (DWLSSVR). First, sample weights are incorporated to minimize the multi-kernel maximum mean discrepancy (MK-MMD) between domains, thereby promoting joint distribution adaptation and decreasing domain discrepancy. Second, the impact of unrelated source domain samples is further mitigated by iterative weights derived from fitting errors. In addition, a two-step strategy is developed to optimize the hyperparameters in DWLSSVR, which introduces a new criterion based on Wasserstein distance (WD). A numerical simulation demonstrates the effectiveness of the developed framework. Then, the proposed method is applied to the small sample modeling of a complex chemical process. The results of predicting NOx emissions from a coal-fired boiler demonstrate that the DWLSSVR model achieves superior prediction accuracy, with a coefficient of determination (R2) of 0.942 under the new operating condition. In contrast, the best LSSVR model achieves an R2 of 0.844 under the same condition.

Geometric deep learning for molecular property predictions with chemical accuracy across chemical space

Chemical engineers heavily rely on precise knowledge of physicochemical properties to model chemical processes. Despite the growing popularity of deep learning, it is only rarely applied for property prediction due to data scarcity and limited accuracy for compounds in industrially-relevant areas of the chemical space. Herein, we present a geometric deep learning framework for predicting gas- and liquid-phase properties based on novel quantum chemical datasets comprising 124,000 molecules. Our findings reveal that the necessity for quantum-chemical information in deep learning models varies significantly depending on the modeled physicochemical property. Specifically, our top-performing geometric model meets the most stringent criteria for “chemically accurate” thermochemistry predictions. We also show that by carefully selecting the appropriate model featurization and evaluating prediction uncertainties, the reliability of the predictions can be strongly enhanced. These insights represent a crucial step towards establishing deep learning as the standard property prediction workflow in both industry and academia.Scientific contributionWe propose a flexible property prediction tool that can handle two-dimensional and three-dimensional molecular information. A thermochemistry prediction methodology that achieves high-level quantum chemistry accuracy for a broad application range is presented. Trained deep learning models and large novel molecular databases of real-world molecules are provided to offer a directly usable and fast property prediction solution to practitioners.

(Invited) Machine Learning in Chemistry: Reactive Force Fields for Carbon Structure Formation

Machine learning (ML) became a premier tool for modeling chemical processes and materials properties. ML interatomic potentials have become an efficient alternative to computationally expensive quantum chemistry simulations. In the case of reactive chemistry designing high-quality training data sets is crucial to overall model accuracy. To address this challenge, we develop a general reactive ML interatomic potential through unbiased active learning with an atomic configuration sampler inspired by nanoreactor molecular dynamics. The resulting model is then applied to study five distinct condensed-phase reactive chemistry systems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early-earth small molecules. In all cases, the results closely match experiment and/or previous studies using traditional model chemistry methods. Importantly, the model does not need to be refit for each application, enabling high throughput in silico reactive chemistry experimentation.

An Interpretable Light Attention–Convolution–Gate Recurrent Unit Architecture for the Highly Accurate Modeling of Actual Chemical Dynamic Processes

To equip data-driven dynamic chemical process models with strong interpretability, we develop a light attention–convolution–gate recurrent unit (LACG) architecture with three sub-modules—a basic module, a brand-new light attention module, and a residue module—that are specially designed to learn the general dynamic behavior, transient disturbances, and other input factors of chemical processes, respectively. Combined with a hyperparameter optimization framework, Optuna, the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process. The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models, including the feedforward neural network, convolution neural network, long short-term memory (LSTM), and attention-LSTM. Moreover, compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1, the LACG parameters are demonstrated to be interpretable, and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM. This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling, paving a route to intelligent manufacturing.

Modelling chemical processes in explicit solvents with machine learning potentials

Solvent effects influence all stages of the chemical processes, modulating the stability of intermediates and transition states, as well as altering reaction rates and product ratios. However, accurately modelling these effects remains challenging. Here, we present a general strategy for generating reactive machine learning potentials to model chemical processes in solution. Our approach combines active learning with descriptor-based selectors and automation, enabling the construction of data-efficient training sets that span the relevant chemical and conformational space. We apply this strategy to investigate a Diels-Alder reaction in water and methanol. The generated machine learning potentials enable us to obtain reaction rates that are in agreement with experimental data and analyse the influence of these solvents on the reaction mechanism. Our strategy offers an efficient approach to the routine modelling of chemical reactions in solution, opening up avenues for studying complex chemical processes in an efficient manner.

System simulation and parametric analysis of low-concentration coal mine gas fed SOFC-CHP with deoxygenation and enrichment pretreatment

Solid oxide fuel cell (SOFC) combined heat and power (CHP) can efficiently convert low-concentration coal mine gas (LC-CMG) into clean energy, avoiding the greenhouse effect of LC-CMG. Deoxygenation and methane enrichment pretreatment of LC-CMG can prevent explosion and anode oxidation risk. Herein, a chemical process model of LC-CMG fed SOFC-CHP system with the pretreatment was established to predict the performances under different operating conditions. Performance evaluation and exergy analysis were performed considering the use of oxygen-rich gas from the pretreatment at SOFC cathode. The effects of key operation parameters on system performances were investigated in terms of residual oxygen concentration, inlet methane concentration, fuel flow and fuel utilization. The results indicated that net electrical efficiency and heat supply efficiency can respectively reach 49.13 % and 23.90 % using LC-CMG of 15 % CH4 with enhancing effect of oxygen-rich gas at cathode. The decrease of methane concentration and the increase of residual oxygen concentration and flow rate caused the decreased electrical efficiency but the increased heat supply efficiency due to the enlargement of concentration polarization and heat supply. The optimal operation parameters were recognized by comprehensively considering diverse performance indicators. This work can guide system integration and operation optimization of LC-CMG fed SOFC-CHP.

ИСПОЛЬЗОВАНИЕ ИСКУССТВЕННЫХ НЕЙРОННЫХ СЕТЕЙ ДЛЯ ПОИСКА РЕШЕНИЙ ДИФФЕРЕНЦИАЛЬНЫХ УРАВНЕНИЙ

An approach to modeling chemical technological processes for calculating solutions of differential methods using artificial neural networks is considered. The capabilities of the Keras module of the Python programming language within these problems are described

All‐nonlinear static‐dynamic neural networks versus Bayesian machine learning for data‐driven modelling of chemical processes

AbstractIn recent decades, the utilization of machine learning (ML) and artificial intelligence (AI) approaches have been explored for process modelling applications. However, different types of ML models may have contrasting advantages and disadvantages, which become critical during the optimal selection of a specific data‐driven model for a particular application as well as estimation of parameters during model training. This paper compares and contrasts two different types of data‐driven modelling approaches, namely the series/parallel all‐nonlinear static‐dynamic neural network models and models from a Bayesian ML approach. Both types of AI modelling approaches considered in this work have shown to significantly outperform several state‐of‐the‐art steady‐state and dynamic data‐driven modelling techniques for various performance measures, specifically, model sparsity, predictive capabilities, and computational expense. The performances of the proposed model structures and algorithms have been evaluated for two nonlinear dynamic chemical engineering systems—a plug‐flow reactor for vapour phase cracking of acetone for production of acetic anhydride and a pilot‐plant for post‐combustion CO2 capture using monoethanolamine as the solvent. For the validation data from the CO2 capture pilot plant, root mean squared error (RMSE) for flue gas outlet temperature, flowrate and CO2 concentration is 0.05%, 1.07%, and 5.0%, respectively, for the all‐nonlinear static‐dynamic neural networks and 0.1%, 1.75%, and 14.14%, respectively, for the Bayesian ML models. For the plug flow reactor data, the Bayesian ML models yield superior RMSE compared to the all‐nonlinear static‐dynamic neural networks when the measurement data are corrupted with Gaussian, auto‐correlated, or cross‐correlated noise.

Annular finite-time stability for IT2 fuzzy networked switched system via non-fragile AETS under multiple attacks: Application to tank reactor chemical process model

This study examines the issue of annular finite-time H∞ performance for interval type-2 fuzzy (IT2) fuzzy networked switched systems vulnerable to multiple attacks via non-fragile adaptive event-triggered control. An adaptive event-triggered scheme (AETS) is presented to minimize the impact of multiple attacks and it effectively reduces redundant data transmission while guaranteeing reliable system performance which differs from the traditional event-triggered scheme. We describe the impact of deception and reply attacks by introducing two random variables that satisfy the Bernoulli distribution. Furthermore, IT2 fuzzy networked switched systems were developed to demonstrate the effects of multiple attacks, and the IT2 fuzzy model describes the nonlinear characteristics. Also, a delay-dependent Lyapunov functional with a free-weighting matrix technique are used. There are limited study findings on the class of IT2 fuzzy networked switching systems using adaptive event-triggered scheme. This paper discusses the advantages of establishing an efficient adaptive event-triggered mechanism for IT2 fuzzy networked switching systems based on previous discussions and necessity. Furthermore, some sufficient conditions that are required to ensure that the system is annular finite-time stable with the stated H∞ performance index is presented along with two examples to demonstrate the suggested control design methodology.

Reliable identification based intelligent PID tuning for long-period process control under different working conditions

BackgroundChemical process models change frequently with operating conditions. However, the industry mostly adopts an off-line calibration and online fixed value commissioning scheme, which can be difficult to maintain control quality. Therefore, a long-cycle online intelligent PID controller calibration scheme is necessary. MethodsFirstly, a high-order linear dynamic model is used as the identification model, and online recursive identification technology is utilized. Secondly, a comprehensive performance index is selected to ensure both stability and rapidity of dynamic transitions. Finally, the slow rate updated PID parameters are obtained during the process control, and the Levy Memory Particle Swarm Optimization (LMPSO) search is applied to harvest the optimal solution. Significant findingsThis paper has presented an intelligent PID tuning method based on reliable identification technology. The main contributions include: (i) By using Lyapunov theory, the boundedness of identification error using the proposed algorithm is guaranteed; (ii) The LMPSO algorithm is proposed to enhance global search capability and search efficiency; (iii) A novel optimization scheme is proposed for a full-process online closed-loop intelligent PID controller. The scheme aims to improve the industrial controller's performance without altering its structure.

Modeling and simulation of a novel chemical process for clean hydrogen and power generation

The present work aims to develop a novel chemical process for clean hydrogen and power production and simulate it accordingly through a unique thermodynamic equilibrium model. This particular process is based on a partial oxidation of hydrogen sulfide (H2S) at superadiabatic conditions to study its respective chemical products. The simulation of superadiabatic partial oxidation of H2S is developed through the present model for the first time in the Aspen Plus. The process is further studied by varying different operating variables with an overall goal of optimizing the H2S conversion into hydrogen. The developed model predicts a satisfactory H2 production flow rate coupled with a low-sulfur dioxide (SO2) output within the superadiabatic partial oxidation regime at an operating pressure below 0.5 bar. The H2S conversion into H2 is then found to be 23.48 % at 0.25 bar. The overall energy and exergy efficiencies of the system are found to be 87.51 % and 70.08 % respectively. The dissociation of H2S in the presence of stoichiometric air results in elemental sulfur and hydrogen production rates of 0.0019 kg/s and 0.0012 kg/s, respectively.

Cryogenic Process Optimization for Natural Gas Purification: Predictive Modeling of Methane-CO2 Solid-Vapor Phase Equilibrium Using Response Surface Methodology.

This research combines industrial engineering principles with chemical process modeling to explore the capture of CO2 from natural gas under cryogenic conditions. The study specifically investigates the Solid-Vapor (S-V) phase equilibrium in a methane-carbon dioxide (CH4-CO2) system. The study employs Response Surface Methodology (RSM) to develop a robust model for predicting phase behavior in industrial gas separation processes. The model is validated using experimental data, offering enhanced operational insights into cryogenic CO2 capture in industrial applications. The developed RSM model is particularly valuable as it can predict the mole fractions of methane and CO2 at various temperatures and pressures in the solid-vapor region of phase equilibrium, where limited experimental data make it difficult to estimate these components accurately. The key contribution of this study is to validate the RSM model's available experimental data, and the model can further be used to predict the process conditions at which high methane composition (yCH4) can be achieved. The developed model showed good agreement when the results were compared with previous experimental studies. The utilization of chemical engineering data to forecast previously unknown conditions in gas separation processes broadens the scope of industrial process optimization in this work.

Termolecular Eley–Rideal pathway for catalytic oxidation of nitric oxide on [Pt2]0,± dimers using O2

AbstractA comprehensive density functional theory (DFT) investigation of the catalytic oxidation of NO to NO2 on neutral and charged [Pt2]0,± dimers has been considered employing M06L/def2TZVP level of theory. Single as well as co‐adsorption energies of NO and O2 molecules suggest that the traditional Langmuir Hinshelwood (LH) mechanism and less explored termolecular Eley–Rideal (TER) and termolecular Langmuir Hinshelwood (TLH) mechanisms, are suitable for a full catalytic reaction pathway in which two NO molecules are converted to two NO2 molecules, initiated by an activated O2 molecule. Activation barrier reveals that the TER mechanism is found to be more reliable in converting two NO molecules into two NO2 molecules on [Pt2]¯ dimer. In addition to shedding light on the intrinsic characteristics of Pt2 dimers, the study will serve as a benchmark for investigating the oxidation process of NO to NO2 utilizing models of termolecular chemical processes.

UNIFAC PREDICTION OF VAPOR LIQUID EQUILIBRIA INVOLVING GAMMA-VALEROLACTONE DERIVATIVE SYSTEMS

Researchers have studied the hydrogenation of Gamma-Valerolactone (GVL) over Ru/C to produce 2-methyltetrahydrofuran (2-MTHF), a biomass-based platform chemical with potential as a biofuel and green solvent. Other byproducts of this reaction include 2-butanol (2-BuOH), 2-pentanol (2-PeOH), and 1,4-pentanediol (1,4-PDO). In this study, UNIFAC activity coefficient models were used to predict the vapor-liquid equilibrium of several systems included in the process to explain the phase behavior which is important in purification process. The accuracy of the UNIFAC model is tested by comparing the experimental boiling points for the binary system of 2-propanol + 1-butanol and the vapor-liquid equi-librium of the GVL + 2-MTHF system. From this comparison, the Root Mean Square Deviation (RMSD) values for the boiling point measurements are obtained to be 3.153%, and for the liquid and vapor phases in the vapor-liquid equilibrium measurements, the RMSD values are 1.934% and 0.298% respectively. These RMSD values indicate the level of accuracy of the UNIFAC model in representing the experimental data for both boiling point measurements and vapor-liquid equilibrium phase behavior. The prediction results of vapor-liquid equilibrium data for GVL derivative systems showed that the 2-BuOH + 2-MTHF system does form an azeotrope when the mol fraction of 2-BuOH is 0.0031 at 79.780C. The calculation was performed using ChemCAD commercial software for chemical process modeling and simulation

Experimentally validated predictive PI-PD control strategy for delay-dominant chemical processes

Controlling delay dominant chemical processes is a challenging control problem. Hence, a new method for designing a Smith predictor (SP)-based proportional-integral proportional-derivative (PI-PD) control strategy is proposed in this study. The inner-loop PD parameters are determined using the phase margin (PM) and maximum sensitivity (Ms) specifications. To determine the outer-loop PI parameters, the moment matching technique is augmented with maximum sensitivity considerations. The performance and robustness of this design is compared with that of its contemporaries using four benchmark chemical process models including that of a stirred tank reactor and a heat exchanger. The resilience of this control strategy amid uncertainties in process parameters is studied and performance improvement achieved over contemporary works is quantified. Finally, the proposed design is also experimentally validated on a two-tank level control loop.

MALTA: A Zonally Averaged Global Atmospheric Transport Model for Long‐Lived Trace Gases

AbstractWe present a two‐dimensional, zonally averaged global model of atmospheric transport named MALTA: Model of Averaged in Longitude Transport in the Atmosphere. It aims to be accessible to a broad community of users, with the primary function of quantifying emissions of greenhouse gases and ozone depleting substances. The model transport is derived from meteorological reanalysis data and flux‐gradient experiments using a three‐dimensional transport model. Atmospheric sinks are prescribed loss frequency fields. The zonally averaged model simulates important large‐scale transport features such as the influence on trace gas concentrations of the quasi‐biennial oscillation and variations in inter‐hemispheric transport rates. Stratosphere‐troposphere exchange is comparable to a three‐dimensional model and inter‐hemispheric transport is faster by up to 0.3 years than typical transport times of three‐dimensional models, depending on the metric used. Validation of the model shows that it can estimate emissions of CFC‐11 from an incorrect a priori emissions field well using three‐dimensional (3D) mole fraction fields generated using a different 3D model than which the flux gradient relationships were derived. The model is open source and is expected to be applicable to a wide range of studies requiring a fast, simple model of atmospheric transport and chemical processes for estimating associated emissions or mole fractions.

Adaptive soft sensor modeling of chemical processes based on an improved just‐in‐time learning and random mapping partial least squares

AbstractThe just‐in‐time learning‐based partial least squares (JIT‐PLS) has been extensively applied to adaptive soft sensor modeling of complex nonlinear processes. However, it still has the problems of unreasonable relevant samples selection and unsatisfactory local modeling. Aiming at these problems, this paper proposes an improved just‐in‐time learning‐based random mapping partial least squares (IJIT‐RMPLS), including an improved relevant samples selection strategy and a random mapping PLS (RMPLS) model. On the one hand, considering the different correlation degrees between input variables and output variable, this method applies mutual information to evaluate the importance of each input variable and designs a variable‐weighted Euclidean distance to select relevant samples for local modeling. On the other hand, in order to prompt the prediction precision of local soft sensor models, this method combines the idea of nonlinear random mapping in extreme learning machines with PLS and builds a RMPLS with multiple activation functions. Applications on a numerical example and a real chemical process show that the proposed IJIT‐RMPLS has smaller prediction error compared with traditional JIT‐PLS.

Language models can identify enzymatic binding sites in protein sequences

Recent advances in language modeling have had a tremendous impact on how we handle sequential data in science. Language architectures have emerged as a hotbed of innovation and creativity in natural language processing over the last decade, and have since gained prominence in modeling proteins and chemical processes, elucidating structural relationships from textual/sequential data. Surprisingly, some of these relationships refer to three-dimensional structural features, raising important questions on the dimensionality of the information encoded within sequential data. Here, we demonstrate that the unsupervised use of a language model architecture to a language representation of bio-catalyzed chemical reactions can capture the signal at the base of the substrate-binding site atomic interactions. This allows us to identify the three-dimensional binding site position in unknown protein sequences. The language representation comprises a reaction-simplified molecular-input line-entry system (SMILES) for substrate and products, and amino acid sequence information for the enzyme. This approach can recover, with no supervision, 52.13% of the binding site when considering co-crystallized substrate-enzyme structures as ground truth, vastly outperforming other attention-based models.