Industrial Production Processes Research Articles

Coordinated Integration of Agricultural and Industrial Processes: a Case Study of Sugarcane-Derived Production

The coordinated integration of agricultural and industrial processes in plant-derived production can offer a solution toward sustainability. However, it is hard for general practitioners to realize the coordinated integration of these processes just based on the precedent fact. A special form clarifying the functions of the required activities should be shared among the practitioners for deliberate system design. In this study, a function model for coordinated integration was developed using the type-zero language of integrated definition for object-oriented design. Inputs, outputs, controls, and mechanisms for the required activities and the relationship between them were analyzed through modeling, after which the model was verified based on actual historical facts in the Japanese cane sugar industry. Finally, as a case study from a different industry, the applicability and limitation of the function model in the palm oil industry are discussed. Although the validity of the model should be confirmed through accumulating future case studies, the structure of the function model should be common to industrial crop-derived productions.

Coactivation of Multiphase Reactants for the Electrosynthesis of Urea

AbstractN2 and CO2 fixation is an environmentally friendly waste‐to‐energy technology that can replace tedious and demanding industrial urea productive processes. Here, VN‐Cu3N‐300 catalysts with tip and vacancy structures are designed for the coactivation of multiphase reactants in the urea electrosynthesis process. Under environmental conditions, the urea yield is 81 µg h−1 cm−2, this is the first report of high area active electrocatalyst, and the corresponding Faraday efficiency is able to reach 28.7%. The full‐cell electrolysis exhibits good stability, providing a current density of 0.7 mA cm−2 at 2.1 V. The electrolyte after continuous electrolysis for 48 h is subjected to being evaporated and recrystallized, and it is determined by 1H NMR that the purity of urea can reach 100%. Comprehensive analysis shows that the local electric field formed by the tip effect can effectively promote the adsorption and activation of CO2. The presence of surface nitrogen vacancies promotes the formation of *NN* intermediates, ensuring CN coupling, and also optimizing the dissociation process of water, providing a proton supply for the synthesis of urea. Thus, the rate‐determining step is altered and the formation of urea is ensured.

Study on subsurface microcrack damage depth of diamond wire as-sawn sapphire crystal wafers

Sapphire crystals are widely used as window materials for infrared emitting devices, high intensity lasers, optical elements and microelectronic substrates due to the excellent material properties. The subsurface microcrack damage produced by diamond wire saw when cutting sapphire crystal is an important indicator of the cutting quality. In this paper, diamond wire sawing experiments were conducted within the industrial production processing parameters to investigate the as-sawn sapphire crystal wafers subsurface microcrack damage depth (SSD), and a numerical calculation model was established to predict the SSD based on the analysis of the scratching stress field of single abrasive. The effects of the saw wire speed and specimen feed speed, saw wire parameters on SSD were predicted. The results show that, within the parameters studied in this paper, the material was removed in brittle mode, increasing the saw wire speed (from 1000 m/min to 1600 m/min) and reducing the specimen feed speed (from 0.5 mm/min to 0.05 mm/min) can reduce the sapphire as-sawn wafers SSD, reduce the size of brittle tear-shape edge breakage, the number and size of brittle pits on the as-sawn wafer surface are reduced and the surface morphology consistency is improved. Increasing the abrasive density and reducing the abrasive size can reduce the SSD. The study results provide a reference for the reasonable matching of sawing process parameters and saw wire parameters in industry.

Автоматизация работы спредера при обработке крупнотоннажных контейнеров в условиях крена и дифферента судна

Current development of IT technologies makes it possible to automate production processes in the transport industry. Introducing the automated systems helps reduce the downtime of vehicles under loading and unloading operations. Handling various loads or specializing in a certain cargo flow, the terminal operator selects lifting and transport equipment and the degree of its automation. Automation of lifting and transport equipment has been most developed at container terminals, because the container as a cargo unit has certain dimensions, the metal surface of the container reflects the signals of the distance sensors mounted on the cargo handling device (spreader). During the experiments, it was found out that the signal of ultrasonic sensors is reflected from the surface of ice, which can form on the container in winter, laser sensors show the distance to the surface of the container. Installation of laser sensors helps to reduce the working cycle time of lifting and transport equipment, reduce the likelihood of damage to the container. Loading and unloading operations on ships have their own specifics. In case of roll and /or trim, it is difficult to point the spreader at the container, which leads to an increase in the working cycle, therefore, the idle time of the vessel under processing. To solve the problem of reducing the working cycle time of lifting and transport equipment when unloading a vessel with a roll or trim of the vessel, a laboratory sample of the mechanism for stabilizing the spreader relative to the surface of the container has been developed. The stabilization mechanism operates according to the following algorithm: the signal from the laser sensors goes to the microcontroller, which compares the signals and then transmits the signal to the actuator, which stabilizes the spreader parallel to the surface of the container. The proposed device will increase the productivity of lifting and transport equipment when handling ships with roll and trim.

Improving undergraduate graduation chemical design using green techniques based on green engineering principles

Green engineering education is of great significance. The development of the chemical industry has a new direction under the background of engineering back to green engineering and chemistry. The undergraduate graduation chemical design is an important course for the comprehensive application of professional knowledge and bridging to the actual production, and also important support for the cultivation of chemical talent. This paper presents a case in which the improvement of ethyl acrylate process design can be selected as a topic of undergraduate graduation chemical design, which has both an industrial production process foundation that student can learn and engineering design innovation under the guidance of the green engineering concept. After the design study, the results show that the process design of ethyl acrylate supported by new clean catalysis and membrane separation technologies has been improved in reducing process complexity and improving environmental friendliness. Furthermore, the teaching result indicates that the process design reinforces students' green engineering thinking and provides a green engineering teaching format for chemical design.

Enabling blockchain-assisted certificateless public integrity checking for industrial cloud storage systems

Due to the sharp increase in industrial data, cloud computing has been integrated into intelligent Industrial Internet of Things systems to store and process massive data. Since core data are key factors in the industrial production process, any intercepted or altered data may cause severe consequences, and the integrity and confidentiality of outsourced industrial data have become extremely concerning security issues. In this paper, we focus on devising an enhanced blockchain-assisted certificateless public integrity checking (BA-CPIC) mechanism for industrial cloud storage systems. BA-CPIC contributes to checking outsourced industrial encrypted data, and exploits the smart contract in the Ethereum blockchain to provide assistance to users for checking malicious behaviors of auditors (e.g., colluding with industrial cloud servers or lazing checking). Besides, BA-CPIC preserves the identity privacy of users enjoying outsourced storage and blockchain-assisted integrity checking services. By contrast, some misbehaved users in industrial cloud storage systems could also be traced and revoked in due course. BA-CPIC avoids both inherent drawbacks of key escrow and complex certificate management, it is provided corresponding security analysis under the certificateless security model. The performance evaluation demonstrates the feasibility of BA-CPIC in the deployment of industrial cloud storage systems.

Quantification and mitigation of byproduct formation by low-glycerol-producing Saccharomyces cerevisiae strains containing Calvin-cycle enzymes

BackgroundAnaerobic Saccharomyces cerevisiae cultures require glycerol formation to re-oxidize NADH formed in biosynthetic processes. Introduction of the Calvin-cycle enzymes phosphoribulokinase (PRK) and ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) has been shown to couple re-oxidation of biosynthetic NADH to ethanol production and improve ethanol yield on sugar in fast-growing batch cultures. Since growth rates in industrial ethanol production processes are not constant, performance of engineered strains was studied in slow-growing cultures.ResultsIn slow-growing anaerobic chemostat cultures (D = 0.05 h−1), an engineered PRK/RuBisCO strain produced 80-fold more acetaldehyde and 30-fold more acetate than a reference strain. This observation suggested an imbalance between in vivo activities of PRK/RuBisCO and formation of NADH in biosynthesis. Lowering the copy number of the RuBisCO-encoding cbbm expression cassette from 15 to 2 reduced acetaldehyde and acetate production by 67% and 29%, respectively. Additional C-terminal fusion of a 19-amino-acid tag to PRK reduced its protein level by 13-fold while acetaldehyde and acetate production decreased by 94% and 61%, respectively, relative to the 15 × cbbm strain. These modifications did not affect glycerol production at 0.05 h−1 but caused a 4.6 fold higher glycerol production per amount of biomass in fast-growing (0.29 h−1) anaerobic batch cultures than observed for the 15 × cbbm strain. In another strategy, the promoter of ANB1, whose transcript level positively correlated with growth rate, was used to control PRK synthesis in a 2 × cbbm strain. At 0.05 h−1, this strategy reduced acetaldehyde and acetate production by 79% and 40%, respectively, relative to the 15 × cbbm strain, without affecting glycerol production. The maximum growth rate of the resulting strain equalled that of the reference strain, while its glycerol production was 72% lower.ConclusionsAcetaldehyde and acetate formation by slow-growing cultures of engineered S. cerevisiae strains carrying a PRK/RuBisCO bypass of yeast glycolysis was attributed to an in vivo overcapacity of PRK and RuBisCO. Reducing the capacity of PRK and/or RuBisCO was shown to mitigate this undesirable byproduct formation. Use of a growth rate-dependent promoter for PRK expression highlighted the potential of modulating gene expression in engineered strains to respond to growth-rate dynamics in industrial batch processes.

A novel operational modality classification method based on image joint contrast

In the industry process, the operation of the operator contains rich operating experience, which can be divided into different operating modes according to different working conditions. The traditional division of operating modes often only starts from the current state or process knowledge, lacking comprehensive data extraction and understanding, which may lead to inaccurate modal classification or modal omission. When modal classification is performed based on pictures, the classification will be inaccurate due to the missing or errors of picture labels or text information. To solve this problem, this paper proposes a new operation modality classification method based on joint image contrast (OMC-JIC). First, the time series data is symbolized by the method of symbolic approximate aggregation (SAX). The variable curve corresponding to the current symbol is used as a picture, while the variable value is stored as text information. Then the symbolic data name can be regarded as the label of the above image. Finally, we can use the operation modality classification method proposed in this paper to train and test the images with labels and text information, and final operation mode classification results are obtained. In order to further verify the effectiveness of the proposed OMC-JIC method, an industrial methanol production process is considered in this paper to test the methodology, and the proposed OMC-JIC method could receive better classification results and operational assistance comparing with other methods.

Identification of Unknown Abnormal Conditions of Absorption Stabilization System in Catalytic Cracking Process Based on Cyclic Two-Step Clustering Analysis and Convolutional Neural Network

Machine learning for online monitoring of abnormalities in fluid catalytic cracking process (FCC) operations is crucial to the efficient processing of petroleum resources. A novel identification method is proposed in this paper to solve this problem, which combines cyclic two-step clustering analysis with a convolutional neural network (CTSC-CNN). Firstly, through correlation analysis and transfer entropy analysis, key variables are effectively selected. Then, the clustering results of abnormal conditions are subdivided by a cyclic two-step clustering (CTSC) method with excellent clustering performance. A convolutional neural network (CNN) is used to effectively identify the types of abnormal operating conditions, and the identification results are stored in the sample database. With this method, the unknown abnormal operating conditions before can be identified in time. The application of the CTSC-CNN method to the absorption stabilization system in the catalytic cracking process shows that this method has a high ability to identify abnormal operating conditions. Its use plays an important role in ensuring the safety of the actual industrial production process and reducing safety risks.

A spatial–spectral adaptive learning model for textile defect images recognition with few labeled data

Textile defect recognition is a significant technique in the production processes of the textile industry. However, in the practical processes, it is hard to acquire large amounts of textile defect samples. Meanwhile, the textile samples with correct defect labels are rare. To address these two limitations, in this paper, we propose a novel semi-supervised graph convolutional network for few labeled textile defect recognition. First, we construct the graph convolutional network and convolution neural network to extract spectral features and spatial features. Second, the adaptive convolution structure is proposed to generate adaptive kernels according to their dynamically learned features. Finally, the spatial–spectral adaptive unified learning network (SSA-ULNet) is built for limited labeled defective samples, and graph-based semi-supervised learning is constructed. The textile defect recognition model can extract the textile image features through the image descriptors, enabling the whole network to be end-to-end trainable. To evaluate the proposed method, one public dataset and two unique self-built textile defect datasets are used to textile defect recognition. The evaluation results demonstrate that the proposed SSA-ULNet obviously outperforms existing state-of-the-art deep learning methods.

Scheduling and Controlling Production in an Internet of Things Environment for Industry 4.0: An Analysis and Systematic Review of Scientific Metrological Data

To review the present scenario of the research on the scheduling and control of the production process in the manufacturing industry, this comprehensive article has extensively examined this field’s hotspots, boundaries, and overall evolutionary trajectory. This paper’s primary goal is to visualize and conduct an organized review of 5052 papers and reviews that were published between 2002 and 2022. To reveal the “social, conceptual, and conceptual framework” of the production area, identify key factors and research areas, highlight major specialties and emerging trends, and conduct research, countries, institutions, literature keywords, etc., are all used. Additionally, research methodologies are always being improved. The aim of this work is to explore more references for research implementation by analyzing and classifying the present research status, research hotspots, and potential future trends in this field of research.

Multi-sensors data fusion for monitoring of powdered and granule products: Current status and future perspectives

Process Analytical Technology (PAT) is a systematic approach for monitoring of process parameters and product quality attributes and nowadays is considered for continuous processing of many industrial products. It is a mechanism to design, analyse and control manufacturing processes through on-line, in-line, at-line and off-line configurations for monitoring Critical Quality Attributes (CQAs). PAT systems include a combination of reliable in-line sensors, spectroscopic instruments and Multivariate Statistical Methods (MSMs) to provide informative knowledge for quality assessment of powdered and granule products. Nevertheless, monitoring programs of advanced manufacturing processes based on PAT systems typically provide large sets of data which are complex to interpret. The application of appropriate data-driven modelling techniques could assist in the interpretation of complex data matrices to better control of processes. Data fusion is a data-driven approach that could increase performance and robustness of models used for data interpretation to generate more accurate knowledge about process conditions and performance by merging related outputs collected from several instruments and considering synergies from multiple sources. This paper aims at presenting the current state of the art regarding the application of multi-sensors data fusion for powdered and granule manufacturing processes and making a critical review of recent progress and future possible perspectives in this field.

Examination of the environmental behavior of phosphogypsum with the application of lab-scale experiment

Phosphogypsum (PG) is a reject of the phosphoric acid production process in phosphate fertilizer industries. The process results in the production of relatively large quantities of PG that it might cause serious environmental and human health concerns. The data of a laboratory investigation of PG are presented here. Lab-scale experiments with lysimeters were conducted in order to simulate and examine the environmental characteristics and the temporal behavior of PG leachates in terms of physicochemical characteristics and chemical composition. Based on the results, leachates from already deposited for many years PG or its mixture with marble powder, seemed to have better pH and conductivity values and lower elemental concentrations compared to leachates from freshly disposed PG. However, the leachates characteristics improve and stabilize in both cases after four days of irrigation or of 1080–1240 mm of rain. Most major elements were found to have minimal leachability, and the material satisfied the environmental limits for its disposal at landfills for inert and non-hazardous wastes.

Iterative Learning-Based Predictive Control Method for Electronic Grade Silicon Single Crystal Batch Process

The growth process of silicon single crystal (SSC) is a typical batch production process, which has the characteristics of batch size, variety, complex process, and intensive technology. In order to accurately control the diameter and thermal field temperature of the Czochralski (Cz) SSC batch production process to ensure that the actual production requirements of electronic-grade SSC are met, this paper proposes an iterative learning-based batch process predictive control strategy Model Predictive Control -Iterative Learning Control -Extended State Observer (MPC-ILC -ESO). The control strategy consists of two parts. The time axis is a dual Model Predictive Control (MPC) controller, which is mainly used to deal with disturbance suppression and constraints in the production process of a single batch of SSC. The iterative axis is the Extended State Observer (ESO) based Iterative Learning Control (ILC) control algorithm to deal with uncertainty and disturbance estimation and compensation in the multi-batch production process. The control effects of the time axis and the batch axis are superimposed as the final controlled object input to ensure the crystal diameter and stable thermal field temperature. Finally, the effectiveness of the proposed control strategy is verified based on the data analysis of the semiconductor industry production process.

Time Series Prediction Method of Industrial Process With Limited Data Based on Transfer Learning

Industrial time series, as a kind of data that responds to production process information, can be analyzed and predicted for effective monitoring of industrial production processes. There are problems of data shortage and algorithm cold start in industrial modeling process caused by complex working conditions, change of data acquisition environment and short running time of equipment. As a result, the accuracy of the existing data-driven industrial time series prediction algorithm is greatly limited. To address the above problems, we propose a new time series prediction method for industrial processes under limited data based on dynamic transfer learning in this work. This method aims to effectively use historical data of similar equipment or working conditions rather than discard them to help establish an industrial time series prediction model with limited target data. In this method, first, historical data are divided into multiple batches, and then a new multi-source transfer learning framework with dynamic maximum mean difference (MMD) loss is established according to the distribution distance between each batch of historical data and the limited target data at the current moment. The framework also combines multi-task learning methods to establish multi-step prediction model for online learning in industrial processes. Compared with other commonly used methods, experiments on two real-world datasets of solar power generation prediction and heating furnace temperature prediction demonstrate the effectiveness of the proposed method.

热风炉换热器多目标优化

Shell-and-tube heat exchangers are widely used in many research fields and industrial production processes, but little research has been conducted on the use of heat exchangers for drying crops. This study conducted a numerical simulation of the temperature, velocity, and pressure fields based on the shell-and-tube fluids of a heat exchanger in a biomass particle hot-blast stove. The correctness of the simulation results was verified by test data before simulation, and the mesh was verified to be irrelevant. The application of a multi-objective genetic algorithm in heat exchanger design and optimization was explored, considering five design variables, such as hot tube diameter, transverse pitch, longitudinal pitch, cold flow velocity, and hot flow velocity for optimization. The Nusselt number, friction factor, and comprehensive performance coefficient were used as objective functions for 2D and 3D response surface analysis. The final design variables P1=74.91 mm, P2=104.23 mm, P3=121.37 mm, P4=4.83 m/s, and P5=8.48 m/s were obtained to improve the comprehensive performance coefficient by 16.11%. The heat transfer performance was improved by 9.55% and the resistance performance was reduced by 15%.

МИЛЛИЙ САНОАТ ИШЛАБ ЧИҚАРИШ ЖАРАЁНЛАРИНИНГИҚТИСОДИЙ-СТАТИСТИК КЎРСАТКИЧЛАРИ ТИЗИМИ

The system of economic and statistical indicators of national processes of industrial production, the main accounts of the SNA, the main indicators of operational statistics of enterprises and their description, the system of statistical indicators of industrial production are explained in this article.

Industrially Scalable and Refreshable Photocatalytic Foam

AbstractSemiconductor photocatalysis has been a promising candidate for micropollutant degradation in environmental remediation. In the past 50 years, TiO2 nanomaterials‐based photocatalytic devices have been practically applied to ex‐situ sewage treatment. This work promotes the practical photocatalytic application of TiO2 nanomaterials by successfully fabricating a scalable and refreshable photocatalytic foam. The fabrication is done by firmly immobilizing catalyst nanoparticles on ethylene‐vinyl acetate (EVA) foam. The immobilization replaces commonly used ZnO promoter by TiO2 catalyst nanoparticles in raw materials during industrial foaming production processes. The as‐fabricated photocatalytic foam exhibits excellent photocatalytic activity, stability, and refreshability in in‐situ micropollutant degradation.

A Deep Neural Network-Based Intelligent Detection Model for Manufacturing Defects of Automobile Parts

Image defect detection of casting parts is a key part of the production process in the machinery manufacturing industry. The traditional methods are ineffective because traditional computer image processing methods require a large number of manual features to be set artificially, and the detection time is too long. In order to save human resources and improve the efficiency of image defect detection, this paper proposes a deep learning-based defect detection method for automobile parts. This paper selects EfficientNetB0 as the backbone framework of the target detection network, which significantly reduces the memory usage of the model and shortens the model inference time, while improving the model detection accuracy. Facing the problem of small samples of defect image dataset, we analyze the image characteristics of the dataset and introduce shape transformation and scale scaling as the basic online data enhancement method according to the industrial field image projection law. Then, it is expected to combine the traditional image processing algorithms according to the characteristics of casting parts with different depth distribution and multiple morphological changes, and develop a special image defect data enhancement method. This further improves the performance of the model and increases the detection accuracy of the algorithm by 22.3% without increasing the data.

Process function-based selection and configuration of Process Equipment Assemblies (PEAs) demonstrated on an industrial process

Process Equipment Assemblies (PEA) based process design is built on selection of designed equipment from a databank instead of a tailor-made design. The first complete PEA-based selection and configuration method, including the main equipment, auxiliary functions and the infrastructure was presented in [1]. The process function-based planning approach uses automated procedures for estimating the operating range of the separating equipment as basis for the selection. This paper demonstrates the whole methodology on the industrial production process of triallyl cyanurate with more than one process step and different unit operations to show the method´s general applicability. As a basis, databanks for the main separation equipment, the infrastructure and the auxiliary PEAs are introduced. Additionally, a comparison of conventional and PEA-based designed distillation equipment showed comparable results and an applicability of the developed PEA-based tools in conventional design.