Industrial Production Processes Research Articles

Elastico-viscous Buongiorno model nanofluid flow over a stretching sheer with radiative heat transfer phenomena

The current presentation intended for the Walters’ viscoelastic flow of nanofluid over a stretching sheet embedding with permeable medium. Buongiorno model nanofluid associated to both Brownian and thermophoresis affects the flow phenomena because of the communication of the radiative heat as well as the dissipative heat. The reactive species in conjunction with first order chemical reaction enriches the solutal transfer profile. The present study has novel approach because of the current applications in particular the production processes in industries as well as drug delivery system in the flow of blood in artery etc. The assumption of similarity variables is useful for the transformation of the governing equations. Because of the complexity of the problem perturbation technique is employed for the reduction of higher order differential equations to its lower order for the suitable choice of perturbation parameter. Further, shooting based Runge-Kutta technique is used to get the complete numerical solutions. The physical behavior of the effective parameters on the flow phenomenon are obtained and displayed via graphs. The simulated results for the rate coefficients are also deployed via tables. A good correlation is obtained with prior established results to validate the present result. However, the significant characteristics of the important results are; impact of buoyant forces for the conjunction of thermal and solutal buoyancy overshoots the velocity distribution and the fluid temperature augments for the higher values of the thermophoresis parameter whereas reverse impact is rendered for the Brownian motion.

Safety of the Introduction of Self-driving Vehicles in a Logistics Environment

Thanks to the increasing speed of technological development brought by the 21st century, many of the processes in industrial production have been automated. The next big step in this progress of artificial intelligence could potentially be in corporate logistics, road haulage, and even private transport. Naturally, automation within corporations and in critical infrastructure raises ethical and trust-related issues, reflected in the attitude of emerging markets toward this form of development. This research aims to discover the current state of development of self-driving vehicles through comprehensive literary research and to present those ethical problems, the solutions to which are paramount for society to accept and to incorporate the concept of self-driving vehicles. As a result of the analysis of a knowledge-based system with the inductive inference method in primary research, the current strengths and weaknesses of automation in the field of logistics are revealed. The paper summarizes the results of the primary research and projects them for the future of automation.

ANALISIS PERANAN SEKTOR INDUSTRI PENGOLAHAN BANTEN DALAM PEREKONOMIAN BANTEN DAN NASIONAL

The business field or processing industry sector is a strategic sector in the banten economy. In addition to dominating the economy and absorbing a large number of workers, processing industry products are also the main commodities for Banten's foreign exports. This study generally aims to analyze the role of banten processing industry business fields in the scope of the Banten and National economy, using the IRIO Indonesia 2016 Table. Specifically, the purpose of his research is to analyze the relationship between the processing industry sector and other sectors in the Banten economy, as well as its relationship with the national economy. In addition, a measurement of the role of processing industry business fields is also carried out through the identification of leading sectors and analysis of multiplier numbers. The results showed that the sub-fields of the food and beverage industry, the textile and apparel industry, as well as the paper and paper goods industry, printing and reproduction of recording media, have a very high forward and backward linkage with other sectors in Banten, thus becoming Banten's leading sector. However, of these three sub-fields of business, only the paper and paper goods industry, printing and reproduction of recording media also play a role as the national leading sector

Scale-Up to Pilot of a Non-Axenic Culture of Thraustochytrids Using Digestate from Methanization as Nitrogen Source.

The production of non-fish based docosahexaenoic acid (DHA) for feed and food has become a critical need in our global context of over-fishing. The industrial-scale production of DHA–rich Thraustochytrids could be an alternative, if costs turned out to be competitive. In order to reduce production costs, this study addresses the feasibility of the non-axenic (non-sterile) cultivation of Aurantiochytrium mangrovei on industrial substrates (as nitrogen and mineral sources and glucose syrup as carbon and energy sources), and its scale-up from laboratory (250 mL) to 500 L cultures. Pilot-scale reactors were airlift cylinders. Batch and fed-batch cultures were tested. Cultures over 38 to 62 h achieved a dry cell weight productivity of 3.3 to 5.5 g.L−1.day−1, and a substrate to biomass yield of up to 0.3. DHA productivity ranged from 10 to 0.18 mg.L−1.day−1. Biomass productivity appears linearly related to oxygen transfer rate. Bacterial contamination of cultures was low enough to avoid impacts on fatty acid composition of the biomass. A specific work on microbial risks assessment (in supplementary files) showed that the biomass can be securely used as feed. However, to date, there is a law void in EU legislation regarding the recycling of nitrogen from digestate from animal waste for microalgae biomass and its usage in animal feed. Overall, the proposed process appears similar to the industrial yeast production process (non-axenic heterotrophic process, dissolved oxygen supply limiting growth, similar cell size). Such similarity could help in further industrial developments.

A backoff approach to design of optimally flexible water networks under uncertainty

Water management through design of proper water networks is a key to the reduction of freshwater usage and wastewater discharge, and thus, more sustainable production in process industries. In this work, an optimization-based approach to synthesis of water networks under uncertainties (e.g., disturbances) in both contaminants concentrations and flow rates is presented. A backoff procedure from the literature is embedded in the optimization problem in order to guarantee steady-state feasibility of the optimally designed network in spite of the sustained process disturbances. Appropriate reformulations for improving mathematical properties of the problem result in a mixed-integer nonlinear program and enables its global solution using deterministic algorithms. The network structure features tanks with constant-flow pumps and overflow lines for better disturbance handling with little to no need for an active control system, thereby reducing network’s operational complexity. The proposed approach is demonstrated through a number of case studies including a real corn refinery problem with multiple contaminants, where it is shown that an optimally flexible water network can be achieved with a minimal increase (only 6%) in the freshwater consumption compared with a nominally optimal design.

Cross-metathesis of biomass to olefins: Molecular catalysis bridging the gap between fossil and bio-energy

Terminal olefins are important building blocks for the industry of biofuels, oligomers, and lubricants production. The industrial processes for production of olefins involving oligomerization of ethylene or cracking of petrochemical waxes have several flaws including low yield and high cost in product separation. Cross - metathesis of bio - derived unsaturated fatty esters and olefins with ethylene (ethenolysis), allows the conversion of sustainable waste biomass to various renewable olefins with much safer, less toxic, sustainable, and zero-CO 2 emission processes. To our best knowledge, however, a comprehensive summary of key advances in this field (since 2017) is yet to be available, particularly on molecular features of homogeneous and heterogeneous catalysts. This paper presents a critical review on molecular structures of metal complex and oxide catalysts for ethenolysis of olefins and oleochemicals. The influence of cationic centers, coordination conditions, nature of ligands, operating conditions on catalyst performances will be systematically discussed along with relevant reaction mechanism. The key challenges for rational design of coordinated cationic hybrids have been summarized, which will provide insights to technological advancement of large - scale production of oleochemical - derived olefins.

Inovasi Usaha Abon Lele Tingkat Usaha Kecil Menengah (UKM) dalam Peningkatan Kualitas Produk

Industrial development in Indonesia is very dynamic, including in the food and beverage industry. So that innovation and facilitation for Small and Medium Enterprises (SMEs) are needed to improve the competitiveness of the products produced. SMEs shredded catfish "Indri Marwanti" Mojolaban Sukoharjo, is one of the fisheries product processing industries that have made innovations in shredded catfish products. Starting from the arrangement of facilities and production equipment, the application of good manufacturing practice methods, adaptive use of equipment to improve product quality, to packaging design and labeling on its business products. This activity is intended to implement innovations in increasing the quantity and quality of SME shredded Catfish "Indri Marwanti" through improving production methods and introducing several equipment and packaging that can improve the quality of shredded catfish so that consumer acceptance and business turnover can increase.

Optimization of wheat flour production processes in a flour mill industry using linear programming

This study applied linear programming method as an optimization package to analyze the operational processes of wheat flour mill industry, with functional models developed using defined variables constraints of wheat flour x_1, baking flour x_2 and semolina x_3 for objective functions. Furthermore, a proficient operational process of wheat flour mill industry based on raw materials requirements and production cost using linear programming method with MATLAB application to optimize the operational process of wheat flour production plant by maximizing raw material resources and minimizing production cost using a modified milling process flow chart for enhanced products yield from the milling of the whole wheat grains, and a monthly analysis of the available wheat resource for production and machine hours operated during each month. The results after computing the average supply of wheat and available machine hours as variables and modelled into linear programming equations for optimization showed increase in semolina production, reduction in wheat flour production and reduction in production cost of baking flour respectively. In addition, the profit generated showed a significant increase in the months of December at ₦164,850.00 (highest) when the machine hours and wheat available were at its peak. Thus, baking flour was found to be the least profitable among the three products monthly and recommendations suggested a reduction in its production cost to enable profitability in its production

In Situ Measurement of Instantaneous Viscosity Curve of Fluids in a Reserve Tank

A novel method enabling in situ measurement in a reserve tank for evaluating an instantaneous viscosity curve is presented. This aims to satisfy the practical demand of industrial production process for monitoring real-time rheological properties without taking samples. With applicable ranges of shear rate and viscosity, O(10–1–102 s–1) and O(10–2–100 Pa·s), it enables evaluation of fluid properties having shear strain rate dependence, based on ultrasonic spinning rheometry. Since this method evaluates the characteristic of shear thinning viscosity while changing the fluid temperature in a reserve tank, some useful examples are expected as further applications for measuring viscosity during chemical reaction processes, such as gelation, hydrolysis, and polymerization. Efficacy of the method evaluating rheological properties is validated by numerical and experimental results.

Fault monitoring of batch process based on multi-stage optimization regularized neighborhood preserving embedding algorithm

Batch process is an important type of industrial production process, and the process mechanism is complex. It is difficult to accurately describe the dynamic changes of the production process of multi-stage time-varying batch process. In addition, the data of batch process contain not only global information but also local information. The traditional neighborhood preserving embedded algorithm is used to maintain the local geometric structure of data while ignoring the global information, and the extracted latent variables cannot fully characterize batch process. Therefore, we propose a multi-stage optimization regularized neighborhood preserving embedding (ORNPE) algorithm. First, the multiple process stages are separated by affinity propagation (AP) algorithm. Second, based on maintaining local information of neighborhood preserving embedding algorithm, slow feature analysis algorithm is used to extract dynamic time-varying global information. Then, cross-entropy is used to optimize the global information, and the extraction ability of the global information is improved. Finally, a monitoring index based on support vector data description is constructed to eliminate adverse effects of non-Gaussian data for monitoring performance. The effectiveness and advantages of the proposed algorithm based on monitoring strategy are illustrated by the penicillin fermentation process and a semiconductor industry process.

Biodiversidad e industria en la región de Tula, Hidalgo: un ensamblaje sistémico

The objective of the study was to describe the characteristics, under a systemic approach, of two of the most important components of the territory: biodiversity and industry in the region of the Tula basin, Hidalgo, based on extensive documentation from official sources and scientific records to understand the operation of the graphic model: "Industry Biodiversity Interaction System", which was built based on said characterization. The industrial sector in the study region presents a spatial concentration of a dispersed type without being established in a particular way in any municipality. Pachuca and Tepeji del Rio are the municipalities that show a slightly higher industrial presence. The largest industry in the Region is that of products derived from non-metallic minerals, followed by chemicals and plastics. With the exception of Chilcuautla and Progreso de Obregón, the Region has a high rate of anthropogenic impact, which contributes to the fact that the Region has a low and very low ecological integrity. The Region has low vulnerability to climate change, but it is vulnerable to wastewater from Mexico City and the Tepeji del Río aquifer has a deficit in water availability. Therefore, the Region reflects processes of unsustainability and the need to restore it (34% of the area as a priority for restoration), which may be due in part to industrial production processes with a high impact on the natural environment and discharges from Mexico City.

Machine Learning Approach for Classifying Power Outage in Secondary Electric Distribution Network

Power outage is the problem that hinders social and economic development especially for developing countries like Tanzania. Frequent power outages damage electric equipment, and negatively affect the industrial production process. Power outages cannot be completely eradicated due to uncontrolled cause like natural calamities but technical challenges can be managed and hence reducing power outages. The existing manual methods used to locate power outage like customer calls is inefficient and time consuming. On the other hand, modern method like the Advanced Metering Infrastructure (AMI) still faces a challenge in effectively classifying power line outage due to the nature of imbalanced datasets. Therefore, there is a need to develop a Machine Learning (ML) model to accurately classify power line outage. In this study, machine learning models are constructed from ensemble algorithms and tested using outage AMI data from 2012 to 2019 with 2 hours interval records. We propose the following ensemble-based machine learning approach to enhance classification; data sampling, algorithm weighting and finally ensembling. Results show that the Hybrid Stacking Ensemble Classifier (HSEC) model outperforms the others by accuracy of 0.981 G-mean, followed by Extra tree with accuracy of 0.964 G-mean. This model can be used in power line outage classification in any Secondary Electrical Distribution Network (SEDN). This study can be extended to locate power outage to household.

Literature review on sources of interference and proposed solutions for RFID installations in complex production and logistics processes in the automotive industry

RFID (Radio Frequency Identification) is used in complex production and logistics processes in the automotive industry. One of the biggest challenges with RFID installations is the bad performance, especially the low read rates. One reason for the low read rates are sources of interference. In order to reduce sources of interference in RFID installations, these and possible solutions must first be known. Therefore, in this paper a literature review was conducted to identify the known sources of interference and proposed solutions for RFID installations in the automotive sector. These were divided into the four categories RFID components, electrical signals, physical environment and environmental influences. In addition, this paper presents a procedure for reducing sources of interference in RFID installations. With this information, a company can build up know-how and support the reduction of interference sources in RFID installations, which in turn increases the read rate.

Perspectives on nonstationary process monitoring in the era of industrial artificial intelligence

The development of the Internet of Things, cloud computing, and artificial intelligence has given birth to industrial artificial intelligence (IAI) technology, which enables us to obtain fine perception and in-depth understanding capabilities for the operating conditions of industrial processes, and promotes the intelligent transformation of modern industrial production processes. At the same time, modern industry is facing diversified market demand instead of ultra-large-scale demand, resulting in typical variable conditions, which enhances the nonstationary characteristics of modern industry, and brings great challenges to the monitoring of industrial processes. In this regard, this paper analyzes the complex characteristics of nonstationary industrial operation, reveals the effects on operating condition monitoring, and summarizes the difficulties faced by varying condition monitoring. Furthermore, by reviewing the recent 30 years of development of data-driven methods for industrial process monitoring, we sorted out the evolution of nonstationary monitoring methods, and analyzed the features, advantages and disadvantages of the methods at different stages. In addition, by summarizing the existing related research methods by category, we hope to provide reference for monitoring methods of nonstationary process. Finally, combined with the development trend of industrial artificial intelligence technologies, some promising research directions are given in the field of nonstationary process monitoring.

Using ISU-GAN for unsupervised small sample defect detection

Surface defect detection is a vital process in industrial production and a significant research direction in computer vision. Although today’s deep learning defect detection methods based on computer vision can achieve high detection accuracy, they are mainly based on supervised learning. They require many defect samples to train the model, which is not compatible with the current situation that industrial defect sample is difficult to obtain and costly to label. So we propose a new unsupervised small sample defect detection model-ISU-GAN, which is based on the CycleGAN architecture. A skip connection, SE module, and Involution module are added to the Generator, enabling the feature extraction capability of the model to be significantly improved. Moreover, we propose an SSIM-based defect segmentation method that applies to GAN-based defect detection and can accurately extract defect contours without the need for redundant noise reduction post-processing. Experiments on the DAGM2007 dataset show that the unsupervised ISU-GAN can achieve higher detection accuracy and finer defect profiles with less than 1/3 of the unlabelled training data than the supervised model with the full training set. Relative to the supervised segmentation models UNet and ResUNet++ with more training samples, our model improves the detection accuracy by 2.84% and 0.41% respectively and the F1 score by 0.025 and 0.0012 respectively. In addition, the predicted profile obtained using our method is closer to the real profile than other models used for comparison.

(Digital Presentation) Ternary Nifetiooh Catalyst for the Oxygen Evolution Reaction: Study of the Effect of the Addition of Ti at Different Loadings

Ternary NiFeTiOOH Catalyst for the Oxygen Evolution Reaction: Study of the Effect of the Addition of Ti at Different Loadings Wenjamin Moschkowitsch and Lior Elbaz Chemistry Department, Bar-Ilan University, Ramat-Gan 5290002, IsraelThe demand for energy is expected to grow rapidly in the next decades, but it cannot be solely fulfilled with fossil fuel-based technologies without having a huge impact on the environment. The shift to production of clean energy from alternative sources, such as wind and sun, raise the importance of energy storage technologies. One of the most prominent solutions is storing surplus energy, harvested at peak production times and seasons, in hydrogen. However, the production of hydrogen with methods that require as little energy as possible, as well as being sustainable, environmentally friendly and cheap, are still considered to be a big challenge.Water electrolysis is the simplest industrial process for hydrogen production, and can be linked to fuel cells technology. Among the available electrolyzers, alkaline electrolyzers (ALE) are considered state-of-the-art. Although they can work with platinum-group metal-free (PGM-free) catalysts, unfortunately, this technology still requires the use of PGM catalysts in order to increase the current density, and lower the reaction activation energy.In electrolyzers, water splits into oxygen and hydrogen in two separate reactions, taking place at the anode and cathode. The cathodic reaction is the Hydrogen Evolution Reaction (HER), which is considered to be relatively facile. The anodic, Oxygen Evolution Reaction (OER), is considered to be much more difficult, since it is a four-electron process with very sluggish kinetics. The best known catalysts for this reaction in acidic medium are IrO2 and RuO2, oxides of very rare and precious metals (Ir is the scarcest metal on earth’s crust). In addition, in acidic medium, most PGM-free catalysts, based on earth abundant elements, are considered unstable (these conditions have also shown to be detrimental for Ir and Ru-based catalysts). In contrast, in ALEs, PGM-free catalysts have shown to be a good alternative to PGM catalysts. The most common OER PGM-free catalysts are first-row transition metals in their oxide, hydroxide and oxyhydroxide forms.One such catalyst is nickel oxyhydroxide (NiOOH). The structure of this specific catalyst has been studied in great detail by many different research groups, yet there are several open questions regarding the OER mechanism, i.e. the exact catalytic center and active phase.Recent studies suggest that pure NiOOH is not very active at all, and that all of the activity can be attributed to iron impurities.Indeed, NiFeOOH with iron content of 15-25 %at, has a much higher activity and a much lower overpotential compared to other PGM-free catalysts. It can thus be regarded as a benchmark for this class of OER catalysts.It is well accepted by now that the bimetallic catalyst further increases the intrinsic catalytic activity, and that addition of other transition metals,can further increase it.

Application of ultrasound-assisted cryoprotectant impregnation for improving the storage quality of snakehead fish fillets.

In aquatic products companies, saccharides and phosphates are often used to impregnate products to improve their quality. Insignificant impregnation effect is a main problem met in the impregnation process. The effect of ultrasonic-assisted impregnation on the storage quality of snakehead fish fillets at -18 °C was studied. For all samples, such parameters as water holding capacity, pH value, salt-soluble protein content, whiteness and cooking loss rate were examined periodically. Furthermore, the changes of moisture distribution and migration in snakehead fish fillets during storage were investigated using low-field nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI). The results demonstrated that ultrasonic-assisted impregnation showed better anti-freeze and water holding capacity effects than that of cryoprotectant impregnation alone. Therefore, ultrasonic treatment could be used as an effective way to improve the quality of snakehead fish fillets during the frozen process. This study could provide a broad application prospect in aquatic product processing industry.

Simulation and influencing factors of carbon emission peak in Wenzhou’s textile and clothing industry

To reflect the huge pressure on the environment caused by carbon emissions (CEs) in the production process of the textile and clothing industry, this study uses the CE coefficient method to calculate the CEs of the textile and clothing industry in Wenzhou, China from 2004 to 2018. Moreover, this study analyses the decoupling relationship between Wenzhou’s textile and clothing industry and economic growth by using the decoupling theory and using the Laspeyres decomposition method to analyse the impact of CE factors. Research results show as follows: the decoupling of the CE and economic growth in Wenzhou’s textile and clothing industry from 2004 to 2018 was five years of non-decoupling (2005, 2006, 2007, 2012, 2016) and four years of relative decoupling (2008, 2010, 2013, 2015), five years of absolute decoupling (2009, 2011, 2014, 2017, 2018), the overall decoupling state is good; among the main factors affecting CE, the cumulative effect of industrial scale is the key factor that drives the growth of CE. The industry structure effect and technology level effect inhibit the increase in CE, and the technology level effect has a small impact on the CE of the textile and clothing industry. This paper fills in the gaps between the environmental regulation means and methods of pillar industrial clusters in specific regions and provides paths and measures for Wenzhou’s textile industry and regional energy conservation and emission reduction

Efficient synthesis of tyrosol from L-tyrosine via heterologous Ehrlich pathway in Escherichia coli

For the efficient conversion of L-tyrosine (L-Tyr) to tyrosol, which is an aromatic compound widely used in the pharmaceutical and chemical industries, a novel four-enzyme cascade pathway based on the Ehrlich pathway of Saccharomyces cerevisiae was designed and reconstructed in Escherichia coli. Then, the expression levels of the relevant enzymes were coordinated using a modular approach and gene duplication after the identification of the pyruvate decarboxylase from Candida tropicalis (CtPDC) as the rate-limiting enzymatic step. In situ product removal (ISPR) strategy with XAD4 resins was explored to avoid product inhibition and further improve tyrosol yield. As a result, the titer and conversion rate of tyrosol obtained were 35.7 g·L−1 and 93.6%, respectively, in a 3-L bioreactor. Results presented here provide a potential enzymatic process for industrial production of tyrosol from cheap amino acids.

Evaluation of Innovation in the Development of Agricultural Product Processing Industry Programs in Bantaeng Regency

Innovation evaluation consists of a systematic investigation to look at the various objectives of activities and programs, such as enabling learning, development, improvement and capacity, the importance of policies and programs and supporting oversight. This research method uses a qualitative approach and case studies toanalyze and explain phenomenawhich focuses on how to evaluate the innovation of the agricultural product processing industry development program. Data collection is done by conducting interviews and observations. Data processing and analysis techniques are carried out in three stages, namely: data reduction, presentation and drawing conclusions. Informants in this case are as follows: a). The research is as follows: Head of the Bantaeng Regency Agriculture Office. b). Secretary of the Bantaeng Regency Agriculture Office. c). Head of the Facilities and Infrastructure Division of the Bantaeng Regency Agriculture Service. d). CBO (Community Base Organization). The results showed that the framework used to measure the evaluation of innovation in the development of agricultural product industry programs in terms of program development, network expansion, and comprehensive system improvement was effective. Then the involvement of youth groups in socialization and counseling to women farmer groups was not involved. The new approach in evaluating the innovation of the agricultural product processing industry development program in Bantaeng Regency in terms of an economic approach and a social approach has been effective.