Industrial Production Processes Research Articles

Study on the Spatio-temporal Evolutionary Properties of Gas-liquid Two-phase Flow in Centrifugal Pump as Turbine (PAT)

With the increasing complexity of the industrial production process, the transmission medium of the hydraulic turbine is no longer satisfied, and the gas-liquid two-phase mixed medium has to be considered. The presence of gas in the transmission medium will alters the internal flow structure of the hydraulic turbine and affect its operational stability. Therefore, for the purpose of clarifying the influence of inlet gas content on the internal flow of PAT, the unsteady flow of the PAT is simulated in this paper using numerical simulation. Based on the numerical simulation results, the influence of inlet gas content on the internal flow characteristics, characteristics of pressure fluctuation in impeller and volute, and vortex evolution of flow field are analyzed. The accumulation of gas phase leads to the emergence of vortices, and regions with low pressure values appear at the vortex generation. The major factor of the periodic variation of pressure fluctuation between volute and cut-water is the dynamic and static interference of impeller. The increase of gas content causes more flow disorder in the cut-water region and the volute contraction section. Since the gas in the flow channel is predominantly on the suction side of blades, the flow field on the suction side is more complex than that on the pressure side, and the amplitude of pressure fluctuation increases appropriately. The vortex structure is mainly distributed on balance hole, inlet area of impeller and suction side of blade. As the blade rotates, there are new shedding and growth of vortices, and finally attached to the volute wall. Increasing gas content enhances the influence of blade rotation on the vortex evolution characteristics in the volute and impeller.

Effect of pre-strain on hydrogen-induced delayed cracking and hydrogen trapping behavior in high-strength martensitic steels

Pre-strain is a key factor affecting the critical conditions for hydrogen-induced delayed cracking (HIDC). In this study, the threshold stress and critical hydrogen concentration for HIDC of MS1300 and MS1500 steels with different pre-strains were measured by constant load tensile (CLT) tests. The results demonstrated that although MS1500 has higher strength, its safe service strength is lower than that of MS1300 when the hydrogen content exceeds 0.5 wppm. Additionally, the hydrogen penetration, thermal desorption spectroscopy, and hydrogen microprinting methods were also used to analyze the hydrogen-induced failure mechanism. These studies provide an effective data reference for improving the industrial production process.

Biogas production from piloted wood-based bioethanol process using high-rate anaerobic treatment – Focusing for inhibition and improving biodegradability

To promote the bioeconomy, biorefineries with novel processes are developed. Biogas technology is commonly applied in biorefineries, and in this study, its feasibility for biogas production from wastewaters generated in a novel piloted wood-based bioethanol process was examined in expanded granular sludge bed reactors (EGSB). The novel wastewater was found to have high organic matter content and contained potentially inhibitory compounds. Wastewater was diluted with reactor effluent or tap water (control), and the proportion of wastewater in the feed was increased gradually to increase the organic loading rate. The wastewater was acidic (pH 4.2–4.6) and contained high concentrations of soluble chemical oxygen demand (sCOD 21–51 g/L) and sulfate (1–2.5 g/L). EGSB reactors had high sCODww (sCOD in the wastewater) removal of 74–77 % with OLRs up to 22 kg-sCODww/m3d and no inhibition either by the sulfide resulting from the high influent sulfate concentration or by the re-use of reactor effluent was seen. Integration of anodic oxidation (AO) to the EGSB reactor was studied in batch experiments increasing the biodegradability and total sCOD removal to 80–83 % and enhancing the methane production by 3 %. The results provide valuable information to support the implementation of anaerobic treatment plants as part of new industrial scale bioproduct production processes and encourages further studies on AO.

A Review of Polyhydroxyalkanoates: Characterization, Production, and Application from Waste

The search for alternatives to petrochemical plastics has intensified, with increasing attention being directed toward bio-based polymers (bioplastics), which are considered healthier and more environmentally friendly options. In this review, a comprehensive overview of polyhydroxyalkanoates (PHAs) is provided, including their characterization, applications, and the mechanisms underlying their biosynthesis. PHAs are natural polyesters produced by a wide range of prokaryotic and some eukaryotic organisms, positioning them as a significant and widely studied type of bioplastic. Various strategies for the production of PHAs from agroindustrial waste, such as cacao shells, cheese whey, wine, wood, and beet molasses, are reviewed, emphasizing their potential as sustainable feedstocks. Industrial production processes for PHAs, including the complexities associated with extraction and purification, are also examined. Although the use of waste materials offers promise in reducing costs and environmental impact, challenges remain in optimizing these processes to enhance efficiency and cost-effectiveness. The need for continued research and development to improve the sustainability and economic viability of PHA production is emphasized, positioning PHAs as a viable and eco-friendly alternative to conventional petroleum-based plastics.

The efficiency of CUSUM schemes for monitoring the multivariate coefficient of variation in short runs process

Current monitoring technologies emphasize and address the issue of monitoring high-volume production processes. The high flexibility and diversity of current industrial production processes make monitoring technology for small batch processes even more important. In multivariate process monitoring, a broader applicability exists in multivariate coefficients of variation (MCV) based monitoring schemes due to the lower restriction of the process. In view of the effectiveness of MCV monitoring and with the aim to achieve further performance improvement of current MCV monitoring schemes in a finite horizon production, we additionally introduce two one-sided cumulative sum (CUSUM) MCV schemes. In the case of deterministic and random shifts, the design parameters of the proposed schemes are obtained via an optimization procedure designed by the Markov chain method and the corresponding performance is analysed based on different run length (RL) characteristics, including the mean and the standard deviation. Simulation comparisons with existing exponentially weighted moving average (EWMA) MCV schemes show that the proposed CUSUM MCV schemes are more efficient in monitoring most of the shifts, including the deterministic and random shifts. Finally, to demonstrate the benefits of the new monitoring schemes, a comprehensive case study on monitoring a steel sleeve manufacturing process is conducted.

The physical information LSTM surrogate model for establishing a digital twin model of reciprocating air compressors

Reciprocating air compressors play a crucial role in industrial production processes. However, due to the complex structure and long operating time of reciprocating air compressors, real-time monitoring to grasp the operating status of reciprocating air compressors has become particularly important. Digital twin is a technology that can reflect the behavior of physical entities in real time, accurately predicting and evaluating the operation status of reciprocating air compressors. However, the establishment of a digital twin model for reciprocating air compressors requires a significant amount of computational resources, which can result in the inability to meet the requirements of real-time performance evaluation. To overcome this limitation, this paper proposes a method for constructing a digital twin model of reciprocating air compressors based on a surrogate model. The surrogate model is constructed based on a long short-term memory neural network with physical information(PILSTM). This model can accurately describe the changes in cylinder pressure by combining physical information. According to the characteristics of cylinder pressure changes, regularization formulas are added to ensure the smoothness of the predicted pressure. The experimental results show that the digital twin model based on the surrogate model has high prediction accuracy and real-time performance. Therefore, this model provides a new method for monitoring the operating status of reciprocating air compressors.

Effective removal of nitrate by palygorskite-supported nanoscale zero-valent iron from aqueous solution

Nitrate nitrogen (NO3--N) has become an important pollutant in industrial and agricultural production processes in China. In this study, Palygorskite-supported nanoscale zero-valent iron (nZVI/PG) adsorbent was prepared using the liquid-phase reduction method for the removal of NO3--N from water. Benefiting from the good dispersion uniformity of nZVI on the Palygorskite carrier, the prepared nZVI/PG exhibited a significantly increased specific surface area compared to nZVI, with a noticeable reduction in nZVI aggregation. The Fe/PG mass ratio of 2:1 was found to be the optimal loading ratio for the removal of NO3--N by nZVI/PG, with a removal capacity of 24.36 mg/g and a removal efficiency of 81.23 %, with a nitrogen gas conversion rate of 40.92 %. The removal process of NO3--N by nZVI/PG followed a pseudo-second-order kinetic model, with the removal rate inversely proportional to the initial concentration of NO3--N and directly proportional to the dosage. nZVI/PG exhibited high removal efficiency for NO3--N within the pH range of 2 to 9, with the highest selectivity for NO3--N conversion to N2 observed at pH 5. The main mechanism for the removal of NO3--N by nZVI/PG was found to be adsorption and reduction, with the main products being NH4+-N, NO2--N, and N2.

Tracking Differentiator-Based Identification Method for Temperature Predictive Control of Uncooled Heating Processes

The temperature control of uncooled heating processes presents challenges due to a substantial lag and the absence of active cooling mechanisms, which can lead to overshoot and oscillations. To address these issues, we propose an anti-disturbance identification method based on a tracking differentiator (TD) and an input-constrained temperature predictive control (ICTPC) strategy. Our approach specifically considers the impact of unknown disturbances on model identification within a second-order heating process. By employing a TD to differentiate the input and output signals, we effectively minimize the identification error caused by low-frequency disturbances, yielding a robust anti-disturbance identification technique. Following this, we establish input constraints to limit the amplitude and variation of the control input, ensuring a more controlled and predictable system response. Using the identified model, an ICTPC algorithm is designed to achieve stable temperature control in uncooled heating processes. Experimental results from a typical uncooled heating system demonstrate that our method not only significantly reduces overshoot but also effectively mitigates temperature fluctuations, leading to enhanced control performance and system stability. This study provides a practical solution for temperature control in systems without cooling capabilities, offering substantial improvements in the efficiency and quality of industrial production processes.

FORECASTING AND OPTIMIZATION OF CATALYTIC CRACKING UNIT OPERATION UNDER CONDITIONS OF FUZZY INFORMATION

This paper discusses the application of nonlinear regression to forecast and optimize the operation of catalytic cracking units under conditions of fuzzy information. Catalytic cracking is a crucial process in oil refining that produces high-quality gasoline and other light hydrocarbon products. However, the complexity of the process and the uncertainty of initial data complicate the modeling and optimization of plant operations. To address this issue, a nonlinear regression method is proposed that accommodates the fuzziness of input and output parameters described by linguistic variables. The methodology includes the collection and formalization of expert knowledge, the construction of fuzzy models, and their integration into the process control system. Forecasting is performed by creating regression models that describe the relationships between operational parameters and product quality characteristics. The paper presents a procedure for developing and applying nonlinear regression models, describes algorithms for synthesizing linguistic models, and provides examples of their use to optimize the operation of catalytic cracking units. The modeling results demonstrate the high adequacy and accuracy of the proposed method, as well as its advantages over traditional approaches in conditions of uncertainty and data scarcity. The scientific novelty of the research lies in the development and testing of advanced nonlinear regression models adapted for analyzing and optimizing catalytic cracking processes based on fuzzy data. These methods take into account the specificity and uncertainty of process data, improving the accuracy and reliability of forecasts, which facilitates more effective management of production processes in the petrochemical industry. The main reason for conducting this study is the need to improve the control of oil refining processes, particularly catalytic cracking, which plays an important role in producing high-quality gasoline. The complexity of this process and the presence of fuzzy information caused by fuzzy initial data require the development of new modeling and optimization methods. Existing traditional models based on deterministic methods are often insufficient under uncertainty. This leads to a decrease in the accuracy of process control, which can negatively affect the quality of the final product and production efficiency. The use of nonlinear regression in combination with fuzzy logic is a more flexible and adaptive approach that allows you to take into account the fuzziness and uncertainty of data and use expert knowledge to build models that match the actual operating conditions of the units. Thus, this study aims to solve the key problems associated with data uncertainty and the complexity of the catalytic cracking process, which will improve the accuracy of forecasting and optimization of the units. The main contribution is creating a model that uses nonlinear regression methods in combination with fuzzy logic. This allows uncertainty in input data (such as reactor temperature or pressure) to be effectively considered and processed to improve gasoline and other product yield forecasts. It is shown that using nonlinear regression combined with fuzzy logic significantly improves the management of technological processes, increases the output and quality of products, and reduces production costs. The conclusion of the paper discusses the prospects for further development of the methodology and its application to solve similar tasks in other areas of chemical technology.

Key Performance Indicators for Evaluating The Efficiency of Production Processes in Food Industry

Key Performance Indicators for Evaluating The Efficiency of Production Processes in Food Industry

Research on the microscale effects of fuel pellets carrying chemical pesticide distillation residues

To effectively deal with organic hazardous wastes generated in the production processes of chemical pesticide industries. In this study, the strong shear force generated by high-speed rotating blades was utilized to fully mix and stir pasty or viscous liquid organic hazardous wastes with porous solid fuels. The wastes were broken down into small molecular clusters and evenly distributed on the surfaces of the pores of porous solid fuel particles. This study then investigated the thermal characteristics of fuel particles carrying the distillation residues of chemical pesticides under microscale effects, the volatilization and destruction removal characteristics, the formation of pollutants, and the migration and transformation of F and Cl elements. The results showed that when the average diameter of the fuel particles was 6 mm and the mass ratio of the chemical pesticide distillation residue to the fuel particles was 2:3, small molecular clusters of the residue were evenly distributed on the surfaces of the fuel particles, with a dispersity of 67 %. The volatilization rate of the irritating odors in the coated particles was less than 2 %. With an increase in the proportion of the chemical pesticide distillation residue, the ignition temperature of the particles decreased, the thermal stability increased, the burnout temperature rose, and the maximum weight loss rate first increased and then decreased. The Ca and alkali metal oxides in the fuel particles had a significant effect on fixing chlorine and fluorine.

A dynamic multi-objective optimization model for inner-industry carbon responsibility allocation based on carbon tax and carbon offsets accounting

The setting of Intergovernmental Panel on Climate Change (IPCC) raises concerns on curbing the excessive carbon emissions, and European Union (EU) has been proactively attempting “green” initiatives striving to achieve carbon neutrality. The current Carbon Border Adjustment Mechanism (CBAM) only works on short-term carbon reduction without much “greenness” investment incentives so far, and few studies focus on the long-standing inner-industrial carbon reduction coordination. To address these issues, this paper creatively presents a dynamic multi-objective carbon responsibility allocation model (CRAM) for EU carbon market, allows the carbon trade with a taxation based on carbon offsets accounting, and considers the final targets of sustainable “greenness” investment incentives. To find the optimal results of the designed dynamic CRAM, an improved KT-NSGA-II algorithm is proposed to detect the Pareto frontiers of each successive periods. Data from EU Cement and Aluminium industries are then selected for empirical analysis to compare the proposed CRAM to conventional CBAM model. The findings demonstrate that the superiority of CRAM in carbon emissions reduction, economic benefits improvement and green invests encouragement. With the adjustment of the inner-industrial carbon allowance trade, total carbon emissions decreased by 28.03% and the “greenness” investment initiative increased by 39.24% in contrast to the CBAM. The sensitivity analysis of the model also provides suggestions on the settings of carbon quota and tax with different industrial production process, and proposes policy recommendations for CRAM implementation.

Recent Advances in Urea Electrocatalysis: Applications, Materials and Mechanisms†

Comprehensive SummaryUrea plays a vital role in human society, which has various applications in organic synthesis, medicine, materials chemistry, and other fields. Conventional industrial urea production process is energy−intensive and environmentally damaging. Recently, electrosynthesis offers a greener alternative to efficient urea synthesis involving coupling CO2 and nitrogen sources at ambient conditions, which affords an achievable way for diminishing the energy consumption and CO2 emissions. Additionally, urea electrolysis, namely the electrocatalytic urea oxidation reaction (UOR), is another emerging approach very recently. When coupling with hydrogen evolution reaction, the UOR route potentially utilizes 93% less energy than water electrolysis. Although there have been many individual reviews discussing urea electrosynthesis and urea electrooxidation, there is a critical need for a comprehensive review on urea electrocatalysis. The review will serve as a valuable reference for the design of advanced electrocatalysts to enhance the electrochemical urea electrocatalysis performance. In the review, we present a thorough review on two aspects: the electrocatalytic urea synthesis and urea oxidation reaction. We summarize in turn the recently reported catalyst materials, multiple catalysis mechanisms and catalyst design principles for electrocatalytic urea synthesis and urea electrolysis. Finally, major challenges and opportunities are also proposed to inspire further development of urea electrocatalysis technology. Key ScientistsFor urea electrosynthesis, Furuya et al. firstly investigated the electrochemical coreduction of CO2 and NO3−/NO2− using gas‐diffusion electrodes in 1995. Then, Wang et al. effectively achieved C—N bond formation and urea synthesis on PdCu alloy nanoparticles in 2020. Shortly, Yan and Yu et al. proposed the formation of *CO2NO2 from *NO2 and *CO2 intermediates at early stage on In(OH)3 electrocatalyst in 2021, and employed defect engineering strategy to facilitate the *CO2NH2 protonation in 2022. Amal et al. Investigated the role that Cu‐N‐C coordination plays for both the CO2RR and NO3RR. After that, Zhang's group developed In‐based electrocatalysts with artificial frustrated Lewis pairs for urea, and they offered a systematic screening approach for catalyst design in urea electrosynthesis in 2023. And sargent et al. reported a strategy that increased selectivity to urea using a hybrid catalyst.For urea electrooxidation, Stevenson et al. investigated the effect of Sr substitution toward the urea oxidation reaction. Wang et al. provided insights into the urea electrooxidation process using a β‐Ni(OH)2 electrode and Qiao et al. elucidated a two‐stage reaction pathway for UOR in 2021.

Phosphogypsum (PG) crystal size distributions in presence and absence of PG seed crystals and sorbitol surfactant

Leaching of calcium phosphate with sulfuric acid was carried out to study phosphogypsum (PG) crystal growth and size distributions in presence and absence of phosphogypsum seed crystals (PGSC) and sorbitol surfactant (SS) additives. PG crystallization simulates industrial Dihydrate process for phosphoric acid production. PG crystals grow in a medium containing 26±1% P<sub>2</sub>O<sub>5</sub> phosphoric acid and 2±0.5 % free sulfuric acid. Better filterability of phosphoric acid can be achieved with large crystals. The formed PG crystals are larger with addition of either 100 ppm Sorbitol surfactant, 5%, 10% or 15% PGSC compared to the baseline (without additives). With addition of surfactant or increasing the % of PGSC from 5% to 15%, both the mean diameter and d<sub>90</sub> of crystalized PG are increased due to enhancing the crystallization growth rates.

Study on the heating process, high temperature mechanical properties and fatigue properties of a low-carbon microalloyed steel

The dynamic continuous cooling transformation (CCT) experiments of a low-carbon microalloyed building structure steel were conducted on a Gleeble-3500 thermal simulation test machine, and the corresponding dynamic CCT curve was drawn. According to the dynamic CCT curves, different austenitization temperatures were designed to clarify the influence of austenitization temperature on the transformation and microstructure of the tested steel. The results showed that when the austenitization temperatures were set as 1050, 1150 and 1250 °C, respectively, the microstructure mainly consisted of ferrite and granular bainite. With the increase of austenitization temperature, the starting temperature of ferrite transformation gradually decreased, but the starting and ending temperatures of bainite transformation gradually increased, leading to the increase of the volume fraction of granular bainite. In addition, the tensile properties of the tested steel at different temperatures were determined. The tensile strength gradually decreased with the increase of the tensile temperature, and the highest tensile strength was 502 MPa for the room-temperature sample, while the elongation showed different trends. It was the largest elongation (57.8%) at the tensile temperature of 500 °C, while the smallest value of 19.2%–23.2% was obtained in the 200–300 °C tensile samples. Finally, the fatigue property of the experimental steel was detected by plotting the corresponding fatigue curves. The fatigue limit strength under 1 million cycles was about 376.2 MPa. Results provide a theoretical reference for the setting of heating process in the real industrial production and the application of the low-carbon microalloyed building structured steel.

Introduction of a Hydroxyl Self-Sacrificing Surface on Ultrathin Poly(heptazine imide) Nanosheets by KCl Grafting: A New Strategy to Improve the Photocatalytic Production of H2O2.

The generation of H2O2 through photocatalysis is increasingly recognized as a viable approach for addressing the energy and environmental challenges encountered in industrial production processes. In this research, we synthesized ultrathin (1 nm) poly(heptazine imide) (PHI) nanosheets as a photocatalyst by a one-step KCl molten salt process. The utilization of Fourier transform infrared and X-ray photoelectron spectroscopy substantiated that the interlayer-bonded potassium atoms induce polarization in water molecules, facilitating the attachment of hydroxyl groups on the surfaces of nanosheets. These groups serve as self-sacrificing entities, promoting the reaction that leads to the generation of H2O2. The preparation temperature and KCl doping amount factors for the H2O2 generation rate were investigated, and the mechanism of the KCl-bonded structure on photogeneration charge separation transport was analyzed. Owing to the elevated crystallization and the presence of surface self-sacrificing hydroxyl groups, the rate of H2O2 production reaches 6117.5 and 308.35 μmol·g-1·h-1 under visible-light irradiation (λ ≥ 420 nm) in isopropanol solution and pure water, respectively. These rates are 30 and 18.7 times higher than those observed for bulk g-C3N4, respectively. The photocatalytic kinetic processes for H2O2 formation and decomposition were also calculated to investigate the catalyst activities.

Assessing carbon capture and storage technology in industrial joint venture

Carbon Capture and Storage (CCS) technology can effectively reduce carbon dioxide emissions from industrial and energy production processes. Yet the commercialization of CCS technology is hampered by financial requirements. Existing research compares the costs and benefits of business models for individual CCS projects, no studies assess the industry and national levels. Here we use a dynamic computable general equilibrium model, constructure the vertical integration and joint venture business models for CCS. This study assesses the emission and economic impacts of joint ventures between China's high-emission industries and their upstream and downstream sectors. We find that adopting the joint venture model in the coal power sector, compared to the vertical integration model, initially exerts a negative economic impact but can mitigate 0.04 % of GDP by 2060. The chemical industry consistently benefits from the joint venture model both economically and in emission reductions. Downstream sectors of the steel and cement industries are unsuitable for participation in joint ventures. In addition, the joint venture model has significant industry linkage effects, affecting energy consumption, the scale and cost of CCS deployment. This work provides important information for the large-scale commercialization of CCS technology.

Phospholipid biosynthesis regulation for improving pigment production by Monascus in response to ammonium chloride stress.

In the actual industrial production process, the efficient biosynthesis and secretion of Monascus pigments (MPs) tend to take place under abiotic stresses, which often result in an imbalance of cell homeostasis. The present study aimed to thoroughly describe the changes in lipid profiles in Monascus purpureus by absolute quantitative lipidomics and tandem mass tag-based quantitative proteomics. The results showed that ammonium chloride stress (15 g/L) increased MP production while inhibiting ergosterol biosynthesis, leading to an imbalance in membrane lipid homeostasis in Monascus. In response to the imbalance of lipid homeostasis, the regulation mechanism of phospholipids in Monascus was implemented, including the inhibition of lysophospholipids production, maintenance of the ratio of PC/PE, and improvement of the biosynthesis of phosphatidylglycerol, phosphatidylserine, and cardiolipin with high saturated and long carbon chain fatty acids through the CDP-DG pathway rather than the Kennedy pathway. The inhibition of lysophospholipid biosynthesis was attributed to the upregulated expression of protein and its gene related to lysophospholipase NTE1, while maintenance of the PC/PE ratio was achieved by the upregulated expression of protein and its gene related to CTP: phosphoethanolamine cytidylyltransferase and phosphatidylethanolamine N-methyltransferase in the Kennedy pathway. These findings provide insights into the regulation mechanism of MP biosynthesis from new perspectives.IMPORTANCEMonascus is important in food microbiology as it produces natural colorants known as Monascus pigments (MPs). The industrial production of MPs has been achieved by liquid fermentation, in which the nitrogen source (especially ammonium chloride) is a key nutritional parameter. Previous studies have investigated the regulatory mechanisms of substance and energy metabolism, as well as the cross-protective mechanisms in Monascus in response to ammonium chloride stress. Our research in this work demonstrated that ammonium chloride stress also caused an imbalance of membrane lipid homeostasis in Monascus due to the inhibition of ergosterol biosynthesis. We found that the regulation mechanism of phospholipids in Monascus was implemented, including inhibition of lysophospholipids production, maintenance of the ratio of PC/PE, and improvement of biosynthesis of phosphatidylglycerol, phosphatidylserine, and cardiolipin with high saturated and long carbon chain fatty acids through the CDP-DG pathway. These findings further refine the regulatory mechanisms of MP production and secretion.

The Application and Significance of Autonomous Driving Technology in Industrial Production

In this study, we first gave an overview of autonomous driving technology and focused on its application in industrial production. By analyzing the types and characteristics of autonomous driving technology, we introduced key types such as unmanned vehicles, automated robots, and drones in detail. Subsequently, this paper selected three specific application cases of logistics transportation, warehouse management, and production process automation to deeply analyze the actual application effects of autonomous driving technology in industrial production. In addition, this paper also explores the significance of autonomous driving technology in the industrial production process and its impact on industrial production efficiency and cost. The study found that the application of autonomous driving technology can not only improve production efficiency, but also significantly reduce operating costs. Through this study, we hope to provide theoretical guidance and practical reference for the application of autonomous driving technology in the field of industrial production in my country.

An improved industrial fault diagnosis model by integrating enhanced variational mode decomposition with sparse process monitoring method

With the continuous development of intelligent industrial processes, the sparse principal component analysis (SPCA), as a promising process monitoring method, has been widely used in the field of industrial fault detection. However, due to the inadequacy of data preprocessing and the insufficient detection accuracy for minor faults, the SPCA models exhibit obvious limitations in dealing with the processes with dynamic and temporal features. In this study, a Harris Hawk optimization method enhanced variational mode decomposition (HHO-VMD) coupled with the sliding window optimized adaptive SPCA (SWOASPCA) method is proposed to improve the fault detection performance of the SPCA models. In the HHO-VMD-SWOASPCA method, the process data is first preprocessed by adaptively and iteratively optimizing the number of modes and penalty terms in the VMD method via the Harris Hawk Optimization (HHO) method, and then the original SPCA model is combined with the sliding window method and the weight assignment strategy to enhance the model's adaptive capability and accuracy to detect minor faults. Moreover, an improved reconstruction-based contribution (RBC) method is presented to diagnose the detected faults for determining the fault causes. The effectiveness of the proposed method is verified by its application in the industrial sugar production process.