Industrial Production Processes Research Articles

Layer porosity in powder-bed fusion prediction using regression machine learning models and time-series features

Additive manufacturing (AM) using laser powder-bed fusion (L-PBF) has become a common industrial process for high-end component production. The uptake of the process has been accelerated through the broad acceptance of the L-PBF process toward achieving high-quality parts with complex geometry. However, the L-PBF process faces challenges from the process’s sensitivity to the process build parameters, which, when incorrectly set, can cause defects such as porosity, which in turn have a detrimental effect on the produced part properties. On the other hand, the AM processing equipment generates a vast amount of data captured through in situ sensors such as pyrometers and imaging cameras. Having such an abundance of process data facilitates the employment of advanced machine learning (ML) tools to understand and extract patterns and information about the underlying AM process and gain “predictive control.” Driven by this idea, we aimed to employ ML tools over pyrometer time-series data from an L-PBF process to predict the porosity percentage of layers of an AM-built part. Sensor data are naturally modeled by time series; however, most ML algorithms work with tabular data (i.e., one single vector describes a feature). In the work presented here, feature engineering tools were used to transform the time-series data into informative features. These features were fed into the tabular ML algorithms for evaluation, broadening the selection of ML algorithms available in the literature. It was hypothesized that the time-series summary features would capture the interaction of melt-pool temperature with resulting porosity, from which the resulting models could better predict porosity occurrence. The dataset contains layer porosity values in the range of 0.00175 – 7.160%, to which we divide the data into “low” and “high” porous layers using a splitting threshold value of 1%. From evaluating these algorithms, it was concluded that classifying “low” versus “high” porosity layers is relatively easier than predicting the layer’s porosity percentage.

Crystallization of CoWB ternary boride in nickel-based metallic glass

The crystallization kinetics of CoWB ternary boride were investigated through non-isothermal differential scanning calorimetry experiments, using a Ni-based metallic glass precursor as the starting material. The effects of isothermal heat treatment temperature on the phase evolutions were also systematically studied. The results showed that the melt crystallization and glass crystallization behaviors of the alloy differ from each other. The continuous heating transformation diagram plotted from non-isothermal analyses is consistent with the results of isothermal heat treatments. A broad ternary boride precipitation zone was demonstrated, involving the gradual precipitation of CoWB crystals. Avrami exponent values for the early crystallization stage showed that the crystallization mechanism of CoWB is governed by the interface-controlled three-dimensional growth mechanism, and nucleation rate is high. As the crystallization progresses, a diffusion-controlled growth occurs and the nucleation rate decreases. This study offers insights into the heat treatment of CoWB-reinforced nickel matrix nanocomposites, benefiting future research and industrial production processes.

Exploring the Training of Film and Video Talents in Higher Vocational Colleges under the Background of AIGC

With the rapid development of Artificial Intelligence Generated Content (AIGC) technology, the film and video industry's production process and creation mode are profoundly transforming. This technological advancement has put forward new requirements for film and video talent cultivation in higher vocational colleges, prompting the education system to make targeted optimisation in key areas such as curriculum, practical teaching, teacher construction and school-enterprise cooperation. Based on the talent demand of the film and video industry in the context of AIGC, this study analyses the main challenges facing film and Video education in higher vocational colleges, including the disconnection between the curriculum content and the development of the industry, the insufficiency of practical teaching resources, and the weakness of the faculty. Because of these problems, the study proposes the strategies of building an interdisciplinary curriculum system, strengthening practical teaching, deepening university-enterprise cooperation and improving the ‘dual-teacher’ teaching team to provide effective references for the cultivation of high-quality film and video talents with innovative ability and practical skills in higher vocational colleges.

Experimental study on trace moisture control of lithium iron phosphate

LiFePO4 is considered as one of the mainstream positive electrodes materials for power batteries. Its moisture content is closely related to the battery cycle performance and multiplier performance. When the moisture content of LiFePO4 exceeds 600 ppm, the electrochemical performance is seriously degraded. However, it is fairly difficult to reduce its moisture content to below 1000 ppm with the conventional processes. Focusing on the requirements for the trace moisture content of LiFePO4, this study investigated the airflow grinding process inside the closed nitrogen drying system at the first time. The performance in reducing the moisture content in LiFePO4 was also compared. The results demonstrated the moisture content of LiFePO4 powder was lower than 600 ppm after crushing to the particle size of d50 = 1∼1.2 μm and d100 = 6.7–7.1 μm with closed nitrogen drying system under the conditions of the classifier rotation speed 1072 r∙min−1, the steam temperature 120 °C and the pressure 0.5 MPa, and the output capacity reached 210–230 kg h−1. The process gives full play to grinding and drying advantages and break through the drying limit of the traditional processes. It could be thought as a new process for LiFePO4 industrial production, achieving trace moisture drying and relatively high output simultaneously.

Dynamic linkages between element input structure, global value chain, and carbon emissions: evidence from a heterogeneous panel cointegration model

ABSTRACT Industrial CO2 constitutes a dominant component of global CO2 emissions. Therefore, modifications in industrial production processes are crucial. We analyse global cross-national panel datasets from 1995 to 2018 using the panel autoregressive distributed lag (ARDL) model to provide comprehensive evidence of the combined effects of input-side element structures and production-side global value chain (GVC) divisions on output-side CO2 emissions. The results show that: (i) Renewable energy and service elements in production factors can reduce CO2 emissions, though their abatement effects vary. Both can produce sustained carbon mitigation effects in the long term, but only renewable energy is effective in the short term. (ii) The GVC division pattern can induce a CO2 emission increase in both the short and long term, though these effects are heterogeneous depending on the differences in forward-backward GVC divisions. (iii) A path dependence analysis shows that input-side element structures can change production-side GVC divisions, thereby affecting output-side emissions. This study extends our understanding of the environmental effects of various production links and helps coordinate high-quality economic development and impending environmental constraints.

Polydopamine-incorporated MOF membrane with hydrophilicity for effective oil/water separation and fouling-resistant model analysis and prediction

A large number of oil spills and oily wastewater are discharged, in the modern industrial production process, resulting in serious water pollution. Oily wastewater can be extremely harmful to ecosystems and human health. Oil-water separation has become a major challenge at present, and membrane separation has aroused more and more concern in recent years due to its high economic efficiency. This paper fabricated PES filtration membranes using the novel nanoparticlesbased on both metal–organic frameworks-5 (MOF-5) and polydopamine (PDA) layers as dopants, and then adequately explored the porosity, morphology, separation, hydrophilicity, and fouling-resistant performance of the resultant membranes. In addition, the prepared membranes were used for oil–water separation, including soybean-in-water, petroleum ether-in-water, and gasoline-in-water emulsions. MOF@PDA membranes exhibit high separation efficiency of up to 99.8%. Subsequently, membrane fouling mechanisms were investigated using the Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory, and the molecular mode of interaction with the three oil–water emulsions with the membrane was compared. Newly prepared MOF@PDA membranes have better stability, fouling-resistant, and self-cleaning properties. Finally, an optimal machine learning model for oil–water separation efficiency was developed with a high prediction accuracy of 98%. The obtained results indicate that mixed matrix membranes exhibit excellent oil–water separation performance, demonstrating great application prospects in the remediation of oily wastewater.

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.

Control and monitor bottle filling, capping and labelling machine using a programmable logic controller and human machine interface

Bottle filling, closing, and labellingmachine control and monitoring tool using Programmable Logic Controller (PLC) and Human-Machine Interface (HMI). This tool is designed to optimize the production process in the bottled beverage industry,with dimensions of 200 cm × 70 cm × 100 cm. The conveyor transports bottles through the filling, capping, and labeling units. In each unit, sensors are installed to detect the presence of bottles. Bottle liquid volume is determined through time settings inputted through the HMI,and the filling is performed by a water pump. After filling, bottles are capped and tightened mechanically using an air impact system. Labeling is handled by two DC motors which unroll the label and rotate the bottle, ensuringthe label adheres evenlyto all sides of the bottle. The number of bottles produced is calculated using a capacitive proximity sensor and displayed on the HMI. Testing results show that the system effectivelycontrols and monitors the bottle filling, capping, and labeling process viaPLC and HMI. Based on the test results on the bottle filling unit, the system can fill water into the bottle with a maximum percentage error of 0.5% on a 1000 ml volume bottle. In the capping unit, the success rate achieved is 70%, while the success rate in the bottle labeling unit is 80%.

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.

Estimation of atmospheric lead emissions from coal in China: from past to the future under China’s Dual-Carbon policy

Lead, known for its widespread application and high toxicity, has long been a focus of research. Since the prohibition of leaded gasoline in 2000, China has witnessed a shift in the primary sources of atmospheric lead emissions towards coal consumption. We conducted a systematic study on the abundance and distribution of lead in Chinese coal and used model analysis to predict atmospheric lead emissions from different sources. Provincially, the lead content in coal exhibited considerable variation, ranging from 5.27 μg/g to 128.94 μg/g. When incorporating coal reserves in distinct regions as weighting factors, the weighted average lead content in Chinese coal was calculated to be 14.03 μg/g, exceeding the global average by over 80%, which has resulted in substantial atmospheric lead emissions through coal combustion in China. Among the sectors utilizing coal, industrial activities emerged as the foremost contributor to atmospheric lead emissions, accounting for over 90% of the overall release. Considering China's Dual-Carbon Policy, it is expected that lead emissions from coal consumption will decrease by 15%-20% from the peak in 2014 to 2030 but will still approach 10,000 tons. Considering various technologies and carbon emission targets based on China's published policies, the most optimistic emission reduction scenario envisions a decline in coal usage to 190 million tons by 2060, resulting in atmospheric lead emissions of a mere 517.01 tons—a noteworthy reduction to one-twentieth of the present level. Therefore, by controlling coal consumption, especially the use of coal in industrial production processes, and implementing pollution control measures, it is possible to effectively reduce atmospheric lead emissions in the near future under China's two-carbon goal.

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.