Industrial Production Processes Research Articles

CFD-DPM data-driven GWO-SVR for fast prediction of nitrate decomposition in blast furnaces with nozzle arrangement optimization

In the industrial production process, it is important to improve the productivity of blast furnaces in response to the worldwide call for energy saving and emission reduction. In this study, a data-driven method using computational fluid dynamics (CFD) is developed for the rapid prediction of nitrate decomposition in a blast furnace. The method includes the creation of a CFD database, for which nozzle arrangement patterns, relative distances, spray cone angles and temperatures are simulated and collected. Support vector regression (SVR) hyperparameters are optimized using genetic algorithm (GA), particle swarm optimization (PSO), and gray wolf optimizer (GWO). Machine learning interpretability methods shapley additive explanations (SHAP) and partial dependence plots (PDP) are used to analyze the impact of features on prediction results. The results show that the hybrid GWO-SVR model has a better performance, with the degree of fit (R2) improving from 0.7752 to 0.9719. When the distance is taken around 0.58, increasing the temperature and cone angle is more favorable to improve the decomposition rate of the blast furnace. SHAP method shows that only the arrangement 2 and 4 have a positive effect on improving the decomposition rate. The response time for accurate prediction of nitrate decomposition rate is 259200 times shorter than that of industrial-scale CFD simulations. The method is also used to determine the best combination of solutions. This cross-disciplinary approach provides a time-saving and cost-effective tool for improving the response of blast furnace productivity to changes in nozzle arrangement.

Multiple steady states and metabolic switches in continuous cultures of HEK293: Experimental evidences and metabolomics

The optimization of the production process in the biotechnology industry needs a deep understanding of the metabolic patterns developed in the bioreactors. In particular, the possibility to induce changes between different metabolic states in these cultures has opened a new path to reach this optimization. The goal is to drift the culture toward a metabolic state of maximum productivity. In this work we experimentally explore and analyze this path using a HEK293 cell line, transfected to produce the HER1 glycoprotein, cultured in continuous mode. We first show that this cell culture exhibits steady state multiplicity, i.e., different cell densities and protein concentrations for the same experimental parameters. We also demonstrate that the switch between these steady states can be triggered manipulating the dilution rate in the bioreactor. Furthermore, we present an extensive metabolic characterization of the steady states measuring metabolic concentrations through Liquid Chromatographic-Mass Spectrometry (LC-MS). The data obtained is processed using Principal Component Analysis (PCA), unveiling a correspondence between the culture multiplicity and the existence of distinctive metabolic states. Our results support the idea that different stationary states, although obtained for the same experimental parameters, are consistent with major metabolic readjustments in the culture. Finally, the comparison of the different steady states demonstrates that, in this case, the state with reduced lactate production benefits volumetric productivity.

How Can We Control Precision in the ECM Process?

Electrochemical machining (ECM) technology has witnessed several research and development breakthroughs since its introduction in early 1900s. The ECM process serves as one of the key production processes in aerospace, die-moulds, tooling and automotive industries. Over time, the demands on precision have increased and this has resulted in several developments focusing specifically on improving precision in ECM processing. However, the existing review papers have not extensively focused on the methods and parameters controlling precision in the ECM process. Therefore, this article reviews the aspects of precision of ECM processes. This article will serve as a quick-read reference for researchers/engineers looking for precision ECM. An attempt is made to answer the main question posed by industry and academia “How can we control precision in the ECM process”?

Realizing of ZSM-5 microspheres with enhanced catalytic properties prepared from iron ore tailings via solid-phase conversion method.

The comprehensive utilization of iron ore tailings (IOTs) not only resolved environmental problems but also brought huge economic benefits. In this study, the synthetic route presented herein provides a novel method for the synthesis of ZSM-5 microspheres from IOTs. The effects of Si/Al molar ratios and the pH of the precursor solution on the formation of zeolite was evaluated by various analytical methods. The catalytic performance of the catalyst prepared by the solid-phase conversion method (denoted as MP-ZSM-5) was evaluated by methanol-to-propylene (MTP) reaction. Compared with the zeolite catalyst that synthesized via the conventional hydrothermal method (denoted as HM-ZSM-5), MP-ZSM-5 not only prolongs catalytic lifetime from 18.7 to 36.0h but also has higher selectivity for propylene by MP-ZSM-5 (43.7%) than that for HM-ZSM-5 (38.6%). In addition, Kissinger-Akahira-Sunose (KAS) model is applied to the TG result to study the template removal process kinetics. The average activation energy values required for the removal of CTAB and TPABr are 201.11 ± 13.42 and 326.88 ± 16.91kJ∙mol-1, respectively. Furthermore, this result is well coupled with the model-free kinetic algorithms to determine the conversion and isoconversion of the TPABr and CTAB decomposition in ZSM-5, which serves as important guidelines for the industrial production process.

Unleashing the potentials of digital twinning in the production of composite aircraft components

The aircraft industry is continuously challenged to deliver more complex products in order to meet increasingly stricter regulations and growing customer demands, to provide better quality products in larger quantities, and to pay more attention to reusability and circularity issues. At the same time, the industry needs to work with lower costs, shorter lead and production times, less waste, delay, energy consumption, and emissions, and reduce its dependence on non-renewable materials. To keep up with these challenges, the industry automates and continuously innovates as much as is possible. The increasing use of composites in aircraft and aircraft components adds the extra challenge of rapidly bringing innovative manufacturing processes to the factory floor. Aircraft product design, analysis, testing, and production are increasingly being digitized and automated, and consequently involve, and produce, more and more data. Digital twinning is an important enabler for mastering and exploiting this ever-growing plethora of data. In this paper we discuss the potentials of digital twinning in innovative production processes in the aircraft industry, and describe first-hand results and experiences in developing and using digital twinning in the production of composite aircraft components, illustrated by concrete example applications.

Ways to increase the efficiency of industrial production of combined feed

This paper aims at finding a solution to the main problem of feed production: increasing the efficiency of the production process while improving the quality of products. The article aims to develop an adequate mathematical model of the technological process of combined feed industrial production. The authors have developed the theoretical background for describing this process on the basis of flow models within the framework of the original “three effects” concept, a stochastic model has been created taking into account the preservation of the exergy balance of the technological system. The main research tool is the apparatus of system analysis and the theory of random processes. An engineering-mathematical calculation of the parameters of the constructed model was performed, verification of the stochastic model of the technological process of preparing compound feeds in laboratory conditions was carried out. As a result of the research, the authors established the possibility of controlling the stochastic feed flow in all subsystems of the production technological system, outlined ways to solve the problem of increasing the efficiency of feed production by reducing energy costs and improving the output product granulometric composition.

IMPACT OF TOBACCO EXCISE ON SECTORAL OUTPUT AND GROSS INCOME OF INDONESIA : AN APPLICATION OF INPUT OUTPUT ANALYSIS

(Excise on Tobacco Products (CHT) as the largest contributor to the Indonesian economy when compared to other excisable goods reaches 96% so it is considered quite effective to improve the country's economy. CHT has increased every year with the aim of controlling the negative impact of cigarette consumption and relaxing the success of the High Risk Excise Control (PBHT) in eradicating illegal cigarettes. This study aims to analyze the impact of the CHT increase on sectoral output and Gross Domestic Product. The method of analysis in this study is Input-Output using the 2010 IO table (updated in 2019). The classification of the Input Output table is 185 sectors and aggregated into 21 sectors. The analysis in this study is multiplier analysis, distribution and linkage. The result is that the increase in CHT on the tobacco product processing industry has no impact on the national economy as a whole, so it is better for the CHT increase regulation to remain in effect. The government must supervise related parties in the production of the cigarette industry so that fraud does not occur if the CHT increase is still in effect.

CFD simulation and optimization of turning different waste gases into energy in an industrial steam methane reformer

BackgroundOne of the most important processes of industrial hydrogen production is steam methane reforming (SMR). However, the biggest drawback of steam reformer processes is high energy consumption. In methanol production units, 40% of the energy consumed in the steam reformer is provided by methane burning. The best solution to reduce the consumption of methane gas is to use the waste gases of the units. InnovationTo boost the thermal performance of the furnace, as well as the reduction in natural gas utilization, the effects of the kinds of fuel in an industrial unit are investigated. Furthermore, the waste gas is studied to whether it can be used as fuel in an SMR or not. SignificanceCFD is one of the most powerful tools for simulation of the industrial by which a new unit can be set up or the performance of the old units can be developed. Additionally, the effect of diverse parameters on industrial processes can be examined, and the optimal operating conditions of the industrial unit can be found. MethodsIn this study for five different cases of fuel, the furnace of an industrial SMR unit, at the operating conditions, was simulated using Computational Fluid Dynamics, (CFD) and the results were validated by the industrial data. Moreover, regarding the higher thermal efficiency of the furnace and the lower consumption of natural gas, two cases called design, and the suggested modes, were investigated and suggested mode was proposed as the most suitable fuel. FindingBased on the results, removing purge gas and adding waste gases with lower hydrogen content are recommended. Compared with the operational fuel, the suggested mode decreases the flue gas temperature by 11.78 K and increases the temperature of the first row of the reformer tubes by about 4.17 K, while declining the natural gas molar flow rate by 32%. A 64% reduction in hydrogen content of the suggested mode leads to an increase 10% flame length and 14% enhancement of adsorbed radiative heat flux. Moreover, in the suggested mode it is possible to recycle the purge gas to the methanol synthesis reactor and enhance the methanol production to 1% which is 50 ton/day.

Robust Surveillance Schemes Based on Proportional Hazard Model for Monitoring Reliability Data

Product reliability is a crucial component of the industrial production process. Several statistical process control techniques have been successfully employed in industrial manufacturing processes to observe changes in reliability-related quality variables. These methods, however, are only applicable to single-stage processes. In reality, manufacturing processes consist of several stages, and the quality variable of the previous stages influences the quality of the present stage. This interdependence between the stages of a multistage process is an important characteristic that must be taken into account in process monitoring. In addition, sometimes datasets contain outliers and consequently, the analysis produces biased results. This study discusses the issue of monitoring reliability data with outliers. To this end, a proportional hazard model has been assumed to model the relationship between the significant quality variables of a two-stage dependent manufacturing process. Robust regression technique known as the M-estimation has been implemented to lessen the effect of outliers present in the dataset corresponding to reliability-related quality characteristics in the second stage of the process assuming Nadarajah and Haghighi distribution. The three monitoring approaches, namely, one lower-sided cumulative sum and two one-sided exponentially weighted moving average control charts have been designed to effectively monitor the two-stage dependent process. Using Monte Carlo simulations, the efficiency of the suggested monitoring schemes has been examined. Finally, two real-world examples of the proposed control approaches are provided in the study.

Toward More Sustainable Hydraulic Binders: Controlling Calcium Sulfate Phase Selection via Specific Additives

Bassanite (calcium sulfate hemihydrate) is one of the most extensively used inorganic binders in construction applications. Current industrial processes for the large-scale production of the mineral rely almost exclusively on the thermal dehydration of gypsum (calcium sulfate dihydrate), which consumes considerable amounts of energy. Here, we show that phase-pure bassanite can be obtained in high quantities by spontaneous precipitation from supersaturated solutions at moderate temperatures, where gypsum usually forms as a predominant solid phase. Key to control over phase selection is the presence of specific additives during crystallization, which carry functional groups for binding onto calcium sulfate surfaces as well as additional moieties that withdraw water in the local microenvironment. Some of the investigated additives allowed bassanite to be recovered in large amounts at temperatures as low as 40 °C and added concentrations of only 0.1 M or even less. The concepts described in this work pave the way toward alternative approaches, enabling a less energy-intensive and thus more sustainable production of bassanite for use in construction and other applied systems.

Solid-State Fermentation: Applications and Future Perspectives for Biostimulant and Biopesticides Production.

With the expansion of the green products market and the worldwide policies and strategies directed toward a green revolution and ecological transition, the demand for innovative approaches is always on the rise. Among the sustainable agricultural approaches, microbial-based products are emerging over time as effective and feasible alternatives to agrochemicals. However, the production, formulation, and commercialization of some products can be challenging. Among the main challenges are the industrial production processes that ensure the quality of the product and its cost on the market. In the context of a circular economy, solid-state fermentation (SSF) might represent a smart approach to obtaining valuable products from waste and by-products. SSF enables the growth of various microorganisms on solid surfaces in the absence or near absence of free-flowing water. It is a valuable and practical method and is used in the food, pharmaceutical, energy, and chemical industries. Nevertheless, the application of this technology in the production of formulations useful in agriculture is still limited. This review summarizes the literature dealing with SSF agricultural applications and the future perspective of its use in sustainable agriculture. The survey showed good potential for SSF to produce biostimulants and biopesticides useful in agriculture.

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.