Complex Chemical Processes Research Articles

Development of Software for Hydrometallurgical Calculation of Metal Extraction

Hydrometallurgy plays a critical role in the metallurgical industry by providing an efficient method for extracting metals from ores and secondary materials using aqueous solutions. This approach is particularly advantageous for processing low-grade and complex ores, as well as secondary resources that cannot be effectively processed by traditional pyrometallurgical methods. The objective of this study is to develop specialized software to automate and optimize the calculations necessary for metal extraction in hydrometallurgical processes. The software integrates a complete computational framework for automating the various stages of the hydrometallurgical process, including ore composition initialization, total element mass calculations, and the determination of metal concentrations in both metal products (matte) and by-products (slag). The methodology involves the design and implementation of a web-based application using Django for the user interface and MySQL for robust data storage. The computational module, written in Python, automates the mathematical operations required to simulate complex chemical reactions and metal extraction processes. This module supports real-time processing and ensures accurate calculations for each stage of metal extraction, including leaching, extraction, re-extraction, sorption, and electrolysis. The software also features detailed tables and dynamic graphs that allow users to analyze the distribution of valuable metals and assess the influence of key operational parameters on extraction efficiency. The results show that the developed software successfully manages large datasets, enhances the precision of hydrometallurgical calculations, and minimizes the risk of human error. The implementation of the software leads to significant improvements in the economic efficiency of production by optimizing metal recovery rates and reducing operational costs. Additionally, it supports comprehensive process control by providing actionable insights into each stage of extraction, thus improving the quality of the final metal product. Overall, the software represents a significant technological advancement in the field, offering a scalable solution for industries aiming to streamline hydrometallurgical processes.

Direct Recycling of LixNi0.5Mn0.3Co0.2O2 from Production Scrap and End‐Of‐Life Batteries, Using Solid‐State Relithiation

AbstractDirect recycling of Li‐ion battery cathodes offers a sustainable and potentially cost‐effective alternative to conventional methods, often involving complex chemical processes and significant material losses. This study focuses on the relithiation of cathode materials from quality control reject (QCR) and end‐of‐life (EoL) Li‐ion cells to restore their physical structure, morphology, and electrochemical performance. Two NMC532/graphite pouch cells from the same manufacturer were studied. QCR cells, stored under ambient conditions, experienced corrosion and degradation before recycling, while EoL cells were cycled to the end of life. The cells were disassembled, and the cathode materials were delaminated using NaOH solution, then relithiated with LiOH at 700 °C for 15 hours in the air. Extensive characterization analyzed elemental composition, structural properties, thermal stability, and particle size distribution. Results indicated that relithiation successfully restored lithium content and improved the structural ordering and morphology of the cathode materials. The electrochemical performance of the relithiated cathodes exhibited good stability over 100 charge‐discharge cycles. The relithiated QCR samples achieved a capacity of 155.57 mAh g−1, while EoL samples reached 152.53 mAh g−1, comparable to pristine materials. This study highlights relithiation's potential to extend the lifecycle of Li‐ion cathodes, contributing to a more sustainable circular economy for battery materials.

Transitory sensitivity in automatic chemical kinetic mechanism analysis

AbstractDetailed chemical kinetic mechanisms are necessary for resolving many important chemical processes. As the chemistry of smaller molecules has become better grounded and quantum chemistry calculations have become cheaper, kineticists have become interested in constructing progressively larger kinetic mechanisms to model increasingly complex chemical processes. These large kinetic mechanisms prove incredibly difficult to refine and time‐consuming to interpret. Traditional sensitivity analysis on a large mechanism can range from inconvenient to practically impossible without special techniques to reduce the computational cost. We first present a new time‐local sensitivity analysis we term transitory sensitivity analysis. Transitory sensitivity analysis is demonstrated in an example to accurately identify traditionally sensitive reactions at an 18,000x speed up over traditional sensitivities. By fusing transitory sensitivity analysis with more traditional time‐local branching, pathway, and cluster analyses, we develop an algorithm for efficient automatic mechanism analysis. This automatic mechanism analysis at a time point is able to identify the reactions a target is most sensitive to using transitory sensitivity analysis and then propose hypotheses why the reaction might be sensitive using branching, pathway, and cluster analyses. We implement these algorithms within the reaction mechanism simulator (RMS) package, which enables us to report the automatic mechanism analysis results in highly readable text formats and in molecular flux diagrams.

Directional Transportation Behavior of Droplets on a Magnetically Responsive Micropillar Array Surface.

Technologies for manipulating droplets have applications in various fields, such as biomedical devices, chemical engineering, water collection, and thermal management. The morphology of magnetically responsive surfaces are modified using magnetic fields for inducing droplet directional rolling and bouncing. Herein, a study on the directional transport of droplets on magnetically responsive surfaces is reported, identifying three movement modes and analyzing the mechanisms influencing the transport behavior. The study explored working fluids with surface tension lower than that of water, achieving the directional transport of 30% ethanol droplets with a maximum transport velocity of 130 mm s-1. The practical applications of the droplets on a modified surface were analyzed, and methods were developed to accelerate droplet mixing. This study explored efficient operations and automation of complex chemical processes.

Structural Uncertainty in the Sensitivity of Urban Temperatures to Anthropogenic Heat Flux

AbstractOne key source of uncertainty for weather and climate models is structural uncertainty arising from the fact that these models must simplify or approximate complex physical, chemical, and biological processes that occur in the real world. However, structural uncertainty is rarely examined in the context of simulated effects of anthropogenic heat flux in cities. Using the Weather Research and Forecasting (WRF) model coupled with a single‐layer urban canopy model, it is found that the sensitivity of urban canopy air temperature to anthropogenic heat flux can differ by an order of magnitude depending on how anthropogenic heat flux is released to the urban environment. Moreover, varying model structures through changing the treatment of roof‐air interaction and the parameterization of convective heat transfer between the canopy air and the atmosphere can affect the sensitivity of urban canopy air temperature by a factor of 4. Urban surface temperature and 2‐m air temperature are less sensitive to the methods of anthropogenic heat flux release and the examined model structural variants than urban canopy air temperature, but their sensitivities to anthropogenic heat flux can still vary by as much as a factor of 4 for surface temperature and 2 for 2‐m air temperature. Our study recommends using temperature sensitivity instead of temperature response to understand how various physical processes (and their representations in numerical models) modulate the simulated effects of anthropogenic heat flux.

Assuring optimality in surrogate‐based optimization: A novel theorem and its practical implementation in pressure swing adsorption optimization

AbstractSurrogate‐based optimization has gained significant traction in several engineering fields, given its ability to handle complex systems without significant computational burdens. However, surrogate models are approximations and may have limitations, including the possibility of artificial minima. The main contribution of this work is the derivation of a robustness test that guarantees the optimality of surrogate‐based optimization. The derivation of this metric is based on the universal approximation theorem. The full framework proposed in this work is also composed by a sampling sizing methodology to randomly select samples within a feasible operating region (FOR) resulting from the optimization population, reducing the computational cost of the analysis and avoiding biases in the robustness calculation. The applicability and importance of this methodology are demonstrated through a case study of a complex chemical process—a pressure swing adsorption (PSA) unit—which presents a high computational cost to solve optimization problems. The results highlight the need and importance of evaluating the optimality of surrogate‐based optimization schemes.

CFD Simulation of Pre-Chamber Spark-Ignition Engines—A Perspective Review

The growing demand to reduce emissions of pollutants and CO2 from internal combustion engines has led to a critical need for the development of ultra-lean burn engines that can maintain combustion stability while mitigating the risk of knock. One of the most effective techniques is the pre-chamber spark-ignition (PCSI) system, where the primary combustion within the cylinder is initiated by high-energy reactive gas jets generated by pilot combustion in the pre-chamber. Due to the complex physical and chemical processes involved in PCSI systems, performing 3D CFD simulations is crucial for in-depth analysis and achieving optimal design parameters. Moreover, combining a detailed CFDs model with a calibrated 0D/1D model is expected to provide a wealth of new insights that are difficult to gather through experimental methods alone, making it an indispensable tool for improving the understanding and optimization of these advanced engine systems. In this context, numerous previous studies have utilized CFD models to optimize key design parameters, including the geometric configuration of the pre-chamber, and to study combustion characteristics under various operating conditions in PCSI engines. Recent studies indicate that several advanced models designed for conventional spark-ignition (SI) engines may not accurately predict performance under the demanding conditions of Turbulent Jet Ignition (TJI) systems, particularly when operating in lean mixtures and environments with strong turbulence–chemistry interactions. This review highlights the pivotal role of Computational Fluid Dynamics (CFDs) in optimizing the design of pre-chamber spark-ignition (PCSI) engines. It explores key case studies and examines both the advantages and challenges of utilizing CFDs, not only as a predictive tool but also as a critical component in the design process for improving PCSI engine performance.

Research on a semi-supervised soft sensor modelling method for complex chemical processes based on INGO-VMD-ESN

Abstract The dynamic and non-linear nature of complex chemical processes often leads to low prediction accuracy of key quality variables by traditional soft sensors, thus affecting the overall system control accuracy and operational efficiency. Therefore, this paper proposes a semi-supervised soft sensor modelling method based on improved the northern goshawk optimization (INGO)-variable mode decomposition (VMD)-echo state network (ESN). Firstly, a new semi-supervised fusion method is proposed to address the problem of model training difficulty due to the scarcity of labelled samples and process dynamics, which reconstructs the sample dataset by fusing labelled and unlabelled samples into more representative new samples, improving the model’s generalization ability. Secondly, for the noise interference present in the reconstructed data, the input data is denoised using the VMD method to improve the quality of data. Then, a soft sensor model is built based on ESN. Additionally, the denoising and prediction performance of VMD and ESN is significantly affected by parameters, therefore the paper utilizes the INGO algorithm to achieve parameter rectification for VMD and ESN. Finally, the method is validated based on actual sulphur recovery data from a refinery. The results demonstrate that the method effectively mitigates the impact of dynamics and nonlinearity in the complex chemical process which enhances prediction accuracy.

Study on powdering properties of silicone rubber insulating material in a coastal area

Abstract Silicone rubber insulation materials are widely used in energy systems. The powdering of silicone rubber sheds is a newly discovered aging phenomenon in recent years, and there is no specific research. In this paper, the powder composition of the composite insulators in coastal areas was studied, and the powdering process of silicone rubber under a high humidity environment of salt fog was repeated in the laboratory. The results show that the powders can be divided into two types according to particle shape, element composition, and molecule groups: the inorganic filler and its dehydrated product and the small molecule siloxane after polymer aging fracture. The particle size of Type I of powder is 3∼8μm, and the particle size of Type II of small molecule siloxane is 0.3∼1.1μm. The surface hydrophobicity of the powdered sample in a salt-fog environment will deteriorate, and the static contact angle will decrease. However, the hydrophobicity will gradually recover after leaving the harsh environment. Powdering of composite insulator silicone rubber in coastal areas is a complex physical and chemical process caused by the combined action of electricity, heat, and moisture. During this, polymer molecular chains break, and fillers gradually precipitate, forming powders.

CO2 induced phase transition on a self-standing droplet studied by X-ray scattering and magnetic resonance

HypothesisAcoustic levitation is a suitable approach for studying processes occurring at the gas–liquid interfaces, as it allows its investigation in a contact-free manner while providing control over the gas phase. Here, we hypothesize that phase transitions induced by a CO2 rich atmosphere can be examined, at different length scales, in a contact-free manner. ExperimentalA system consisting of 12-hydroxysteric acid (HSA) soaps mixed with different ratios of monoethanolamine (MEA) and choline hydroxide, was prepared. Microliter droplets of the samples were acoustically levitated and monitored with a camera, while exposed to CO2 to modify the pH through diffusion at the air–liquid interface and inside the droplet. The phase transition and water mobility in the levitated droplets were evaluated through X-ray scattering (SAXS/WAXS) and magnetic resonance studies, in real-time. Finally, the droplets were collected and examined under the microscope. FindingsThe introduction of CO2 gas induced a phase transition from micelles to multi-lamellar tubes, resulting in a gel-like behavior both in the bulk and at the interface. The high stability of the acoustic levitator allowed the investigation of this dynamic phenomenon, in real-time, in a contact-free environment. This study showcases the suitability of acoustic levitation as a tool to investigate complex chemical processes at interfaces.

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.

The regeneration of natural stibnite with introduced oxide-based catalyst towards enhanced Li-storage properties

Owing to its excellent theoretical specific capacity, Sb2S3 captures widespread attention in the energy-storage field. However, it still suffers from volume expansion and sluggish electrochemical kinetics. Meanwhile, considering serious pollution and complex chemical preparation processes, stibnite is regarded as “first-hand” materials, displaying enormous energy-storage application potential; however, it is still limited by low-purity and high crystallinity. Herein, stibnite is purified and regenerated through physical chemical and vacuum gas-phase melting strategy to form high-properties stibnite-based electrode materials. Assisted by the introduction of lithium nitrate as an active medium, abundant sites and effective structural traits are formed, effectively promoting the reversibility of electrochemical kinetic processes. Utilized as lithium-ion battery anodes, the as optimized samples have a capacity of approximately 656.8 mAh g−1 with a capacity retention rate of 89.9 % at 0.5 A g−1. Even at 5.0 A g−1, the capacity of 483.3 mAh g−1 could be remained after 100 cycles. Supported by a detailed kinetic analysis, the enhancement of the surface-controlling behavior and the reduction of capacitive resistance are confirmed. Herein, the as-regenerated phase and the introduced oxide-based catalyst are beneficial to alleviate polysulfide shuttling and volume expansion, further accelerating ion/electron transfer behaviors. This work is expected to shed light on strategies to design promising mineral-based anodes for lithium-ion batteries.

A novel fault detection and identification method for complex chemical processes based on OSCAE and CNN

The safety and reliability of chemical processes are of paramount importance. However, due to the complicated heat, mass, and momentum transfer processes coupled with chemical reactions, and the interrelated effects among various equipment units, it is difficult to accurately detect and identify faults occurring in the chemical system. This study proposes an innovative fault diagnosis framework specifically designed for chemical systems. A method based on orthogonal self-attention convolutional autoencoder (OSCAE) is constructed for fault detection. The convolutional autoencoder is combined with an orthogonal attention mechanism to extract time-series characteristic information of the operational state, thereby achieving precise detection of fault conditions. Additionally, a convolutional neural network (CNN) model uses both raw signals and data reconstructed by OSCAE to identify different faults, which enhances the features of the input and enables highly accurate fault identification. The effectiveness and robustness of the propose method is validated with simulation data of our self-established chemical distillation system and the publicly available Tennessee Eastman process system. The diagnosis efficacy in fault detection and identification is validated by applying fault detection metrics, high-dimensional feature visualizations, and confusion matrix analyses across two case studies. This paper not only provides an effective diagnostic framework for chemical systems but also offers a usable benchmark dataset for chemical distillation systems to the academic community, with the hope that the research content can enhance the stability and reliability of chemical processes.

Degradation Studies of Fuel Cell Cathode Catalyst Layers Using Machine Learning

Fuel cells are a promising technology that has the potential to significantly revolutionize the energy sector by providing clean and sustainable sources of power for transportation, aerospace, and electricity applications. However, optimizing the performance of these devices remains a significant challenge as it largely depends on the catalyst layer in the electrodes of these devices, composed of carbon-supported Pt nanoparticle catalyst (Pt/C), perflurosulfonic acid (PFSA), ionomer binder, and a solvent. The catalyst layer design and operation require extensive knowledge of complex physical and chemical processes especially when achieving a low (<0.1 mgcm-2) Pt loading.Furthermore, the degradation of fuel cells is usually associated with either mechanical and/or electrochemical processes of the catalyst layer during operations, ultimately resulting in performance loss. While combinations of factors are responsible for the degradation of the catalyst layer in the fuel cell electrodes, it is still challenging to evaluate which factor contributes the most to the degradation of the catalyst layer.Machine learning algorithms, being powerful tools for predicting and optimizing the behavior of systems can be used to model complex processes, identify optimal operating compositions, and conditions, and reduce the laborious time and cost required for experimentation. They can be used to analyze data obtained from various experiments to identify patterns, trends, and anomalies. In this study, machine learning algorithms are employed for the prediction of catalyst layer degradation. Furthermore, an assessment of feature importance is conducted, elucidating the relative contributions of individual features to the degradation of catalyst layers. Leveraging on machine learning, we can develop an efficient, reliable, and cost-effective fuel cell system.

Integrative catalytic pairs for efficient multi-intermediate catalysis.

Single-atom catalysts (SACs) have attracted considerable research interest owing to their combined merits of homogeneous and heterogeneous catalysts. However, the uniform and isolated active sites of SACs fall short in catalysing complex chemical processes that simultaneously involve multiple intermediates. In this Review, we highlight an emerging class of catalysts with adjacent binary active centres, which is called integrative catalytic pairs (ICPs), showing not only atomic-scale site-to-site electronic interactions but also synergistic catalytic effects. Compared with SACs or their derivative dual-atom catalysts (DACs), multi-interactive intermediates on ICPs can overcome kinetic barriers, adjust reaction pathways and break the universal linear scaling relations as the smallest active units. Starting from this active-site design principle, each single active atom can be considered as a brick to further build integrative catalytic clusters (ICCs) with desirable configurations, towards trimer or even larger multi-atom units depending on the requirement of a given reaction.

Probing the capacity of a spatiotemporal deep learning model for short-term PM2.5 forecasts in a coastal urban area

Accurate forecast of fine particulate matter (PM2.5) is crucial for city air pollution control, yet remains challenging due to the complex urban atmospheric chemical and physical processes. Recently deep learning has been routinely applied for better urban PM2.5 forecasts. However, their capacity to represent the spatiotemporal urban atmospheric processes remains underexplored, especially compared with traditional approaches such as chemistry-transport models (CTMs) and shallow statistical methods other than deep learning. Here we probe such urban-scale representation capacity of a spatiotemporal deep learning (STDL) model for 24-hour short-term PM2.5 forecasts at six urban stations in Rizhao, a coastal city in China. Compared with two operational CTMs and three statistical models, the STDL model shows its superiority with improvements in all five evaluation metrics, notably in root mean square error (RMSE) for forecasts at lead times within 12 h with reductions of 49.8 % and 47.8 % respectively. This demonstrates the STDL model's capacity to represent nonlinear small-scale phenomena such as street-level emissions and urban meteorology that are in general not well represented in either CTMs or shallow statistical models. This gain of small-scale representation in forecast performance decreases at increasing lead times, leading to similar RMSEs to the statistical methods (linear shallow representations) at about 12 h and to the CTMs (mesoscale representations) at 24 h. The STDL model performs especially well in winter, when complex urban physical and chemical processes dominate the frequent severe air pollution, and in moisture conditions fostering hygroscopic growth of particles. The DL-based PM2.5 forecasts align with observed trends under various humidity and wind conditions. Such investigation into the potential and limitations of deep learning representation for urban PM2.5 forecasting could hopefully inspire further fusion of distinct representations from CTMs and deep networks to break the conventional limits of short-term PM2.5 forecasts.

High-resolution urban air pollution mapping.

Variation in urban air pollution arises because of complex spatial, temporal, and chemical processes, which profoundly affect population exposure, human health, and environmental justice. This Review highlights insights from two popular in situ measurement methods-mobile monitoring and dense sensor networks-that have distinct but complementary strengths in characterizing the dynamics and impacts of the multidimensional urban air quality system. Mobile monitoring can measure many pollutants at fine spatial scales, thereby informing about processes and control strategies. Sensor networks excel at providing temporal resolution at many locations. Increasingly sophisticated studies leveraging both methods can vividly identify spatial and temporal patterns that affect exposures and disparities and offer mechanistic insight toward effective interventions. This Review summarizes the strengths and limitations of these methods and discusses their implications for understanding fine-scale processes and impacts.

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.

Transferable preference learning in multi-objective decision analysis and its application to hydrocracking

Hydrocracking represents a complex and time-consuming chemical process that converts heavy oil fractions into various valuable products with low boiling points. It plays a pivotal role in enhancing the quality of products within the oil refining process. Consequently, the development of efficient surrogate models for simulating the hydrocracking process and identifying appropriate solutions for multi-objective oil refining is now an important area of research. In this study, a novel transferable preference learning-driven evolutionary algorithm is proposed to facilitate multi-objective decision analysis in the oil refining process. Specifically, our approach involves considering user preferences to divide the objective space into a region of interest (ROI) and other subspaces. We then utilize Kriging models to approximate the sub-problems within the ROI. In order to enhance the robustness and generalization capability of the Kriging models during the evolutionary process, we transfer the mutual information between the sub-problems in the ROI. To validate the effectiveness as well as efficiency of our proposed method, we undertake a series of experiments on both benchmarks and the oil refining process. The experimental results conclusively demonstrate the superiority of our approach.

Critique: YEASTsim - A Matlab-based simulator for teaching process control in fed-batch yeast fermentations

One of the primary challenges in chemical engineering education lies in the practical application of theoretical knowledge. Contemporary society demands chemical engineers who are not only adept at critical thinking but also proficient in practical problem-solving. Historically, acquiring such practical experience has been heavily dependent on laboratory experiments. However, with the advent of advanced technology, there are now more accessible and varied opportunities for experiential learning. Simulation-based learning tools have proven to be powerful complements to traditional laboratory experiments. These tools enable students to investigate complex chemical processes, test hypotheses, and develop practical skills in a safe, cost-effective, and scalable environment. This critique aims to evaluate the educational potential of the YEASTsim simulator, positioning it as an interactive and innovative tool to enhance the study of core subjects such as Chemical Reaction Engineering, a fundamental component of chemical engineering practice.