Optimization Of Production Process Research Articles

Bacillus spp. as microbial factories for levan and fructooligosaccharide production - Recent trends.

Levan is a fructan-type homopolysaccharide that has gained increasing attention due to its unique properties and promising applications. It is a fructose-based polymer produced through microbial fermentation by diverse microorganisms, including bacteria, yeasts and archaea. The ongoing research on levan mainly focuses on optimizing production processes, elucidating its biological functions, and uncover novel applications. Studies on novel microbial producer strains and fermentation conditions have led to advancements in production processes. Remarkably, the production of levan using the native producer Bacillus spp. has shown promising trends in yield and molecular weight distribution of levan and levan-type fructooligosaccharides. Ongoing studies on levan highlight the importance of understanding the complexities of levan production for commercial scalability. The present article explores the potential of Bacillus spp. as native levan producers, presenting insights into synthesis mechanisms, factors influencing the production of levan, and genetic modifications aimed at enhancing production efficiency and altering properties.

Advances in polyhydroxyalkanoate (PHA) production from renewable waste materials using halophilic microorganisms: A comprehensive review.

Polyhydroxyalkanoates (PHAs) are biodegradable and biocompatible polymers that can replace conventional plastics in different sectors. However, PHA commercialization is hampered due to their high production cost resulting from the use of high purity substrates, their low conversion into PHAs by using conventional microbial chassis and the high downstream processing cost. Taking these challenges into account, researchers are focusing on the use of waste by-products as alternative low-cost feedstocks for fast-growing and contamination-resistant halophilic microorganisms (Bacteria, Archaea…). This is of great importance since these extremophiles can use low-cost substrates, produce high PHA content of copolymers or different PHA monomer compositions. They can present high potential for reducing the costs of PHA fermentation and recovery processes, making their use in commercial applications easier. However, little is known about the potential of halophiles in advancing the PHA production from renewable waste materials at lab-scale and their successful implementation at industrial-scale. This review presents actual advances in PHA production by halophilic pure/engineered species (e.g. Haloferax mediterranei, Halomonas spp.) and mixed microbial consortia (MMC) using organic waste streams. The development of optimal PHA production process involves robust genetic engineering strategies, advanced fermentation processes using mixed microbial consortia versus pure/engineered strains as well as algal biomass as feedstocks.

Predictive Maintenance in Semiconductor Manufacturing: Leveraging IoT Sensor Data for Equipment Reliability

Abstract—Predictive maintenance (PdM) has emerged as a transformative approach in semiconductor manufacturing, leveraging IoT sensor data to enhance equipment reliability and optimize production processes. This review explores the implementation of predictive maintenance strategies in semiconductor manufacturing, focusing on the role of IoT sensor networks, advanced analytics techniques, and integration with Manufacturing Execution Systems (MES). IoT sensor networks enable comprehensive real-time data collection on equipment performance and environmental conditions, thereby providing a foundation for predictive maintenance. Time series forecasting algorithms, such as ARIMA, exponential smoothing, and machine learning-based approaches, are employed to anticipate potential equipment failures. Anomaly detection techniques, including statistical methods and machine-learning algorithms, are used to identify unusual patterns or behaviors that are indicative of impending issues. The integration of predictive maintenance insights with MES allows for real-time decision making, process optimization, and improved overall equipment effectiveness. However, challenges persist in terms of data quality, scalability, and cybersecurity, requiring ongoing research and industry collaboration. Early adopters reported significant reductions in unplanned downtime, optimized maintenance schedules, and improved product quality. As the semiconductor industry continues to evolve, predictive maintenance is expected to play a crucial role in maintaining competitiveness and meeting the growing demand. Further research, standardization efforts, and development of best practices are essential to fully realize the potential of predictive maintenance in semiconductor manufacturing. Keywords—predictive maintenance (PdM), semiconductor manufacturing, IoT sensor data, equipment reliability, forecasting, anomaly detection, unplanned downtime, product quality

Application of Fuzzy Logic-Based Expert Advisory Systems in Optimizing the Decision-Making Process for Material Selection in Additive Manufacturing.

In the era of Industry 4.0, additive manufacturing (AM) technology plays a crucial role in optimizing production processes, especially for small- and medium-sized enterprises (SMEs) striving to enhance competitiveness. Selecting the appropriate material for AM is a complex process that requires considering numerous technical, economic, and environmental criteria. Fuzzy logic-based advisory systems can effectively support decision-making in conditions of uncertainty and subjective user preferences. This study presents a developed advisory system model that uses the Analytic Hierarchy Process (AHP) method and triangular and trapezoidal membership functions, enabling dynamic adjustment of criterion weights. The results demonstrated that the system achieved 85% alignment with user preferences, confirming its effectiveness. Future research may focus on integrating fuzzy logic with machine learning algorithms to further enhance the system's precision and flexibility.

Mechanical Characterization and Computational Analysis of TPU 60A: Integrating Experimental Testing and Simulation for Performance Optimization.

This study investigates the mechanical properties of thermoplastic polyurethane (TPU) 60A, which is a flexible material that can be used to produce soft robotic grippers using additive manufacturing. Tensile tests were conducted under ISO 37 and ISO 527 standards to assess the effects of different printing orientations (0°, 45°, -45°, 90°, and quasi-isotropic) and test speeds (2 mm/min, 20 mm/min, and 200 mm/min) on the material's performance. While the printing orientations at 0° and quasi-isotropic provided similar performance, the quasi-isotropic orientation demonstrated the most balanced mechanical behavior, establishing it as the optimal choice for robust and predictable performance, particularly for computational simulations. TPU 60A's flexibility further emphasizes its suitability for handling delicate objects in industrial and agricultural applications, where damage prevention is critical. Computational simulations using the finite element method were conducted. To verify the accuracy of the models, a comparison was made between the average stresses of the tensile test and the computational predictions. The relative errors of force and displacement are lower than 5%. So, the constitutive model can accurately represent the material's mechanical behavior, making it suitable for computational simulations with this material. The analysis of strain rates provided valuable insights into optimizing production processes for enhanced mechanical strength. The study highlights the importance of tailored printing parameters to achieve mechanical uniformity, suggesting improvements such as biaxial testing and G-code optimization for variable thickness deposition. Overall, the research study offers comprehensive guidelines for future design and manufacturing techniques in soft robotics.

Modelling and optimization of a machine production process: Buffer stock supply implementation and replenishment control

Modelling and optimization of a machine production process: Buffer stock supply implementation and replenishment control

Modern Technology's role in accounting cost calculation of industrial enterprises: Informatization as a key strategy to improve management efficiency.

The study aims to address challenges encountered by modern industrial enterprises, including inefficient accounting cost calculation, delayed information acquisition, and untimely management decisions. By comprehensively applying modern management, information technology, and cost control methods, this study constructs a real-time cost control model to optimize industrial enterprises. Firstly, the model employs mixed integer linear programming (MILP) to optimize production processes through mathematical modeling. Secondly, it utilizes data mining technology to assist enterprises in cost accounting and efficiency analysis. Big data analysis aids in predicting production process changes and trends, providing data support for management decisions. Intelligent production scheduling and resource optimization are facilitated by artificial intelligence (AI) technology, enabling real-time data collection, processing, and analysis. Finally, the activity-based costing method categorizes, allocates, and measures costs to enhance cost accounting accuracy and efficiency. Research results demonstrate that enterprises adopting the theoretical framework of real-time cost control can enhance the accuracy of cost accounting, cost control efficiency, and overall management effectiveness.

Optimization of the concrete production process in terms of energy consumption

Optimization of the concrete production process in terms of energy consumption

Hybrid MCDM-FMEA Model for Process Optimization: A Case Study in Furniture Manufacturing

The furniture-manufacturing industry is pressured to improve quality, productivity, and profitability, particularly within increasingly volatile market conditions. This study is focused on the development of methods for optimizing production processes in a furniture-manufacturing company through the application of an integrated risk management framework. By integrating Failure Mode and Effects Analysis (FMEA) with advanced multi-criteria decision-making (MCDM) techniques, specifically fuzzy AHP, fuzzy TOPSIS, and fuzzy WINGS, a hybrid model is developed to identify, prioritize, and address critical failure points while accounting for complex interdependencies. Significant failure modes, such as order inaccuracies and delivery delays, are revealed as key findings and are found to notably affect productivity and customer satisfaction. The proposed model’s ability to capture cascading effects and a nuanced prioritization enables a more precise risk assessment, thereby supporting resilience and process efficiency in the furniture-manufacturing sector. This approach is shown to not only optimize production but also provide a foundation for applying such hybrid models in other industries to manage sector-specific interdependencies effectively.

Intelligent Control Systems for Mechanical Engineering Technology Tasks

The article is devoted to solving the main tasks set in the work with the aim of analyzing and substantiating the implementation of intelligent control systems in technological processes of mechanical engineering, with an emphasis on increasing efficiency, accuracy, and reliability of production. The use of multi-agent systems and decentralized control systems, which significantly enhance the flexibility and adaptability of production, is analyzed. Special attention is paid to the role of physics-informed neural networks in fault diagnosis, which ensures increased reliability and reduced maintenance costs for equipment. The effectiveness of applying machine learning algorithms to optimize production processes, particularly in material processing and equipment maintenance, is evaluated. The impact of integrating intelligent control systems on production performance and quality, especially in the processes of milling and bonding large parts, is considered. Practical recommendations have been developed for the implementation of an adaptive intelligent production management system (AIPMS), which combines multi-agent systems, neural networks, digital twins, and innovative materials. The implementation of the artificial intelligence concept in production processes will contribute to the further development of mechanical engineering from a technological perspective, enabling enterprises to adopt innovations more rapidly, increase automation, and enhance the adaptability of technological processes, which in turn will lead to significant improvements in product quality and competitiveness. The use of such systems allows optimizing technological processes, reducing the number of defects, lowering energy consumption, and improving environmental efficiency

Carbon Dioxide Upgrading to Biodegradable Plastics through Photo/Electro-Synthetic Biohybrid Systems.

The escalating emissions of anthropogenic carbon dioxide (CO2) and the pervasive issue of nondegradable plastic pollution underscore dual urgent challenges in pursuit of a sustainable society. Achieving such sustainability in the plastic industry, while effectively addressing these environmental concerns, necessitates the development and implementation of innovative strategies for the synthesis of biodegradable polymers utilizing CO2 as feedstocks. The technologies not only facilitate the mitigation of elevated atmospheric CO2 concentrations but also introduce a renewable carbon resource for polymer manufacturing. While considerable research has been undertaken in CO2 upgrading to various C1-3 products, the production of biodegradable polymers from CO2 remains a formidable challenge. Here, we delineate the principal methodologies for catalytic CO2 upgrading to biodegradable polymers, encompassing photocatalytic-biological processes and electrocatalytic-biological approaches. The emphasis of this perspective is on the optimization of production processes and catalyst efficiency, which are paramount in addressing the imperative challenge of decarbonization in the plastic industry, thereby aligning with global environmental sustainability goals.

Carbon Dioxide Upgrading to Biodegradable Plastics through Photo/Electro‐Synthetic Biohybrid Systems

The escalating emissions of anthropogenic carbon dioxide (CO2) and the pervasive issue of nondegradable plastic pollution underscore dual urgent challenges in pursuit of a sustainable society. Achieving such sustainability in the plastic industry, while effectively addressing these environmental concerns, necessitates the development and implementation of innovative strategies for the synthesis of biodegradable polymers utilizing CO2 as feedstocks. The technologies not only facilitate the mitigation of elevated atmospheric CO2 concentrations but also introduce a renewable carbon resource for polymer manufacturing. While considerable research has been undertaken in CO2 upgrading to various C1‐3 products, the production of biodegradable polymers from CO2 remains a formidable challenge. Here, we delineate the principal methodologies for catalytic CO2 upgrading to biodegradable polymers, encompassing photocatalytic‐biological processes and electrocatalytic‐biological approaches. The emphasis of this perspective is on the optimization of production processes and catalyst efficiency, which are paramount in addressing the imperative challenge of decarbonization in the plastic industry, thereby aligning with global environmental sustainability goals.

Цифровизация и использование искусственного интеллекта в производственных процессах современных предприятий

This article analyzes the possibilities and prospects of digital transformation of the Russian manufacturing sector, examines its current state, key driving forces, potential advantages and problems, and provides recommendations for further development. The author examines the issues of introducing the achievements of Industry 4.0 into the production processes of enterprises in traditional sectors of the economy, in particular in transport, energy and mining. As part of the implementation of the achievements of Industry 4.0, it is necessary to mention artificial intelligence (AI), which is becoming an integral element of the modern industrial era, being introduced in order to optimize production processes. In this regard, the author dwells in detail on the use of artificial intelligence, exploring its advantages, opportunities and prospects, as well as showing the best practices of application. At the same time, the author shows and analyzes the main challenges and problems arising from the widespread introduction of artificial intelligence into production processes. The purpose of this article is to consider the relevance of the application of the achievements of Industry 4.0 in modern production, to show the possibilities of using artificial intelligence in production processes, to analyze the main problems that arise and to assess the prospects for further development. Based on the conducted research, it can be concluded that the introduction of Industry 4.0 technologies and other digitalization achievements in modern business, including in manufacturing, transport and energy, provides organizations with serious advantages and opportunities reflected at various levels of management and strategic and operational decision-making. The novelty of this study lies in an attempt to consider the patterns and possibilities of introducing artificial intelligence into the production processes of industrial enterprises, based on their current needs and prospects for further development.

Exploring the Potential of FlexSim 3D Simulation for Robotic Workstation Analysis

Simulators play an important role in testing robotic workstations in production facilities, allowing real-world conditions to be simulated without risk to equipment and staff. They allow users to verify robot control algorithms, identify possible errors, and optimize production processes. By using simulators, companies can reduce prototyping costs and decrease downtime. In addition, it helps to improve safety in production and minimize training costs. The main aims of this research are to analyze and test the FlexSim software, which is a 3D simulator for visualizing and simulating scenarios in manufacturing workplaces. To test the program, a 3D model of a robotic workplace with conveyor belts and CNC machines was created. The simulated workplace was analysed using the analysis tools offered by the software. The results were obtained in the form of graphs, which makes it easier to visually identify downtime, weaknesses, and workstation efficiency. A comparative analysis of our chosen simulator and Tecnomatix Plant Simulation software was also carried out. These two advanced simulators are actively used at serial production plants to analyze and simulate innovative solutions at production locations. At the end of the article, the weaknesses of the two software programs are described, along with a comparative analysis in the form of a table.

Analisis Penyerapan Anggaran Serta Implikasinya Terhadap Kinerja Manajerial

This study examines budget absorption and its implications for managerial performance, focusing on the case of the Tea and Cinchona Research Center (PPTK) Gambung, Bandung Regency, West Java. The research was motivated by the issue of low budget absorption at PPTK, which has impacted operational efficiency and managerial performance outcomes. The independent variables analyzed include sales volume, labor, and raw material costs. Budget absorption is an intervening variable, while managerial performance is the dependent variable. The study employed a quantitative method with descriptive and verificative approaches. Primary data were collected through questionnaires distributed to PPTK managers, and secondary data were obtained from financial and budget reports from 2018 to 2022. Statistical analysis was applied to assess the relationships among the variables. The findings reveal that sales volume, labor, and raw material costs significantly influence budget absorption. Additionally, effective budget absorption positively affects managerial performance. However, challenges such as declining budget absorption percentages and sales volumes in the last two years were identified, indicating room for improvement in budget management. This research recommends adopting more realistic and efficient budget planning, optimizing production processes, and enhancing monitoring mechanisms to improve budget utilization. These measures are expected to strengthen managerial performance and support the organization’s sustainability.

Microalgae for Sustainable Biofuel Generation: Innovations, Bottlenecks, and Future Directions

ABSTRACTMicroalgae, with their rapid growth rates, high intracellular lipid accumulation, and ability to utilize wastewater, emerge as a promising feedstock for high‐quality biodiesel production. Recent contributions in algal fuel research include breakthroughs in genetic engineering to boost lipid yields, such as CRISPR‐modified strains optimized for biofuel production. Innovative bioreactor designs are enhancing scalability and energy efficiency, while integration with waste‐to‐energy systems enables resource recovery and cost savings. Additionally, advancements in techno‐economic models are making large‐scale algal biofuel production more feasible, aided by supportive government policies and subsidies. This review provides a comprehensive analysis of recent advancements in microalgal growth techniques and biofuel production methodologies. This study aims to identify key research gaps and contribute to the optimization of biofuel production processes and the development of sustainable bioenergy solutions.

Применение технологий BigData для оптимизации производственных процессов в горнодобывающей промышленности России: анализ внедрения и эффективности

The article presents a comprehensive analysis of using the BigData technologies to optimize production processes in the Russian mining industry during the period from 2015 to 2024. The main trends and areas of the BigData application, technical and organizational solutions used, barriers and economic effects of their implementation are investigated using an extensive empirical base, including company data, industry surveys and expert assessments. An interdisciplinary approach was applied that combines the methods of statistical analysis, case studies, expert interviews and economic and mathematical modeling. The key success factors of the BigData projects in various segments of the mining industry were identified, and the drivers and barriers of digital transformation of the industry were determined. It is shown that implementation of the BigData leads to a significant increase in productivity, cost reduction and improved safety of mining operations. Recommendations on spreading the best practices were developed and promising directions of the BigData development were justified in conditions of import substitution and transition to Industry 4.0. The results of the study are of high value for the academic community, public policy in the field of digitalization and management practices of mining companies.

Reinforcement learning and digital twin-driven optimization of production scheduling with the digital model playground

Abstract The significance of digital technologies in the context of digitizing production processes, such as Artificial Intelligence (AI) and Digital Twins, is on the rise. A promising avenue of research is the optimization of digital twins through Reinforcement Learning (RL). This necessitates a simulation environment that can be integrated with RL. One is introduced in this paper as the Digital Model Playground (DMPG). The paper outlines the implementation of the DMPG, followed by demonstrating its application in optimizing production scheduling through RL within a sample process. Although there is potential for further development, the DMPG already enables the modeling and optimization of production processes using RL and is comparable to commercial discrete event simulation software regarding the simulation-speed. Furthermore, it is highly flexible and adaptable, as shown by two projects, which distribute the DMPG to a high-performance cluster or generate 2D/3D-Visualization of the simulation model with Unreal. This establishes the DMPG as a valuable tool for advancing the digital transformation of manufacturing systems, affirming its potential impact on the future of production optimization. Currently, planned extensions include the integration of more optimization algorithms and Process Mining techniques, to further enhance the usability of the framework.

Use of aldol condensation between furfural and acetone for biofuel production: A review

The continued reliance on fossil fuels and the associated environmental challenges have spurred the development of renewable energy sources, with biorefineries emerging as a prominent solution for converting biomass into valuable fuels and platform chemicals. Among these, the production of furfural, derived from C5 sugars in lignocellulosic materials, holds particular significance. Extended-chain hydrocarbon fuels and intermediates, such as 4-(2-furyl)-3-buten-2-one (FAc, C8) and 1,4-pentadiene-3-one-1,5-di-2-furanyl (F2Ac, C13), are synthesized via aldol condensation between furfural and acetone, followed by hydrogenation and hydrodeoxygenation processes. This study emphasizes the critical role of catalysts, particularly heterogeneous catalysts, in increasing the efficiency and selectivity of these reactions. Solid catalysts, compared to their homogeneous counterparts, offer substantial advantages, including ease of recovery, reusability, and increased sustainability. Advances in analytical techniques, such as gas chromatography-mass spectrometry (GC-MS) and other state-of-the-art methods, have been instrumental in refining the characterization of heterogeneous catalysts, ensuring improved product quality and process optimization. Additionally, the study explores cutting-edge methodologies for catalyst characterization, utilizing tools such as field emission scanning electron microscopy (FESEM), X-ray photoelectron spectroscopy (XPS), Fourier-transform infrared spectroscopy (FTIR), and inductively coupled plasma optical emission spectroscopy (ICP-OES) to obtain precise quantitative and qualitative insights. This review provides a detailed analysis of the integration of these technologies into biofuel production, highlighting the critical role of innovative catalysts—particularly bifunctional systems under controlled conditions—and the development of optimized conversion routes. These strategies are essential for advancing industrial efficiency, improving process selectivity, and contributing to the sustainability of the energy sector.

Simulation of Triglyceride Purification from Crude Palm Oil using Single Solvent Continuous Counter Current Extraction

As the world’s largest crude palm oil (CPO) producer, Indonesia focuses on palm research, such as CPO-based cooking oil production. Generally, the widely applied production process in the industry is refining, which has several disadvantages, such as low energy efficiency and monochloropropane-1,2-diol (MCPD) formation. Under such conditions, CPO-based cooking oil production methods have been developed, with the continuous countercurrent extraction (CCE) method is considered the most effective for purifying triglyceride (TAG) as the main content of CPO-based cooking oil. The minimum content of TAG in cooking oil is 96% by mass according to SNI 7709:2019 (Indonesian cooking oil standard). Hence, to obtain the optimal production process that meets that standard, a simulation of TAG purification from CPO using the CCE method with a single solvent was conducted. This simulation obtained thermodynamic properties that correspond to the experimental extraction process, in which an industrial scale up can be acquired. The precise thermodynamic properties resulted from the precise selection of property package and Binary Interaction Parameter (BIP) estimation, and those are important because in this extraction process, liquid-liquid equilibrium works. This research used combinations of two property packages: Non-Random Two Liquid (NRTL) and Universal Quasi Chemical (UNIQUAC) and three BIP estimation methods: UNIQUAC functional activity coefficients (UNIFAC), UNIFAC-Lyngby, and UNIFAC-Dortmund. The simulation results were validated using existing experiments with statistical analysis such as relative error, R-Square, and p-value. The most valid simulation results were obtained by NRTL property package and UNIFAC-Lyngby BIP estimation and can be used to develop this TAG purification.