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Due to growing demands for quality, sustainability, and digitalization, data science and artificial intelligence are gaining importance across industries. The extensive product range in many sectors often poses considerable challenges. For example, machine learning (ML) models may struggle with limited data per production variant. The present paper proposes a methodology that integrates the fields of data science and physical simulations. The results from finite element method (FEM) simulations are utilized to transform the process data in such a manner that it can be compared across processes for different production variants and employed for machine learning (ML) methods and statistical analyses. The method is illustrated using an example of aluminum production. A key advantage of this approach is that it can effectively model even production variants with very low quantities. The following discussion will present how this method can be used to enhance production processes, specifically to identify parameters that directly influence product quality, which would not be evident using alternative approaches. Furthermore, the work explores the potential for precisely controlling these parameters using ML models and discusses some major challenges.

Hydrogen is an ideal energy carrier manufactured mainly by the natural gas steam reforming hydrogen production process. The concentrations of CH4, CO, CO2, and H2 in this process are key variables related to product quality, which thus need to be controlled accurately in real-time. However, conventional measurement methods for these concentrations suffer from significant delays or huge acquisition and upkeep costs. Virtual sensors effectively compensate for these shortcomings. Unfortunately, previously developed virtual sensors have not fully considered the complex characteristics of the hydrogen production process. Therefore, a virtual sensor model, called “moving window-based dynamic variational Bayesian principal component analysis (MW-DVBPCA)” is developed for key gas concentration estimation. The MW-DVBPCA considers complicated characteristics of the hydrogen production process, involving dynamics, time variations, and transportation delays. Specifically, the dynamics are modeled by the finite impulse response paradigm, the transportation delays are automatically determined using the differential evolution algorithm, and the time variations are captured by the moving window method. Moreover, a comparative study of data-driven virtual sensors is carried out, which is sporadically discussed in the literature. Meanwhile, the performance of the developed MW-DVBPCA is verified by the real-life natural gas steam reforming hydrogen production process.

Background The biosynthesis of human milk oligosaccharides (HMOs) using several microbial systems has garnered considerable interest for their value in pharmaceutics and food industries. 2′-Fucosyllactose (2′-FL), the most abundant oligosaccharide in HMOs, is usually produced using chemical synthesis with a complex and toxic process. Recombinant E. coli strains have been constructed by metabolic engineering strategies to produce 2′-FL, but the low stoichiometric yields (2′-FL/glucose or glycerol) are still far from meeting the requirements of industrial production. The sufficient carbon flux for 2′-FL biosynthesis is a major challenge. As such, it is of great significance for the construction of recombinant strains with a high stoichiometric yield. Results In the present study, we designed a 2′-FL biosynthesis pathway from fructose with a theoretical stoichiometric yield of 0.5 mol 2′-FL/mol fructose. The biosynthesis of 2′-FL involves five key enzymes: phosphomannomutase (ManB), mannose-1-phosphate guanylytransferase (ManC), GDP- d -mannose 4,6-dehydratase (Gmd), and GDP- l -fucose synthase (WcaG), and α-1,2-fucosyltransferase (FucT). Based on starting strain SG104, we constructed a series of metabolically engineered E. coli strains by deleting the key genes pfkA , pfkB and pgi , and replacing the original promoter of lacY . The co-expression systems for ManB, ManC, Gmd, WcaG, and FucT were optimized, and nine FucT enzymes were screened to improve the stoichiometric yields of 2′-FL. Furthermore, the gene gapA was regulated to further enhance 2′-FL production, and the highest stoichiometric yield (0.498 mol 2′-FL/mol fructose) was achieved by using recombinant strain RFL38 (SG104 ΔpfkAΔpfkBΔpgi119-lacYΔwcaF :: 119-gmd-wcaG-manC-manB , 119 -AGGAGGAGG- gapA , harboring plasmid P30). In the scaled-up reaction, 41.6 g/L (85.2 mM) 2′-FL was produced by a fed-batch bioconversion, corresponding to a stoichiometric yield of 0.482 mol 2′-FL/mol fructose and 0.986 mol 2′-FL/mol lactose. Conclusions The biosynthesis of 2′-FL using recombinant E. coli from fructose was optimized by metabolic engineering strategies. This is the first time to realize the biological production of 2′-FL production from fructose with high stoichiometric yields. This study also provides an important reference to obtain a suitable distribution of carbon flux between 2′-FL synthesis and glycolysis.

The stochastic and undetermined nature of longwall coal mining results from the complex interaction between geological-mining and technical-organizational factors. This interaction causes variability in key parameters of the production process. This article presents three stochastic models developed on the basis of probability density functions, which describe selected process parameters. These mathematical functions serve as the foundation for effective stochastic models, enabling analysis of complex mining operations. The methodology employed in the study involves empirical data collection, statistical analysis, and stochastic simulation, carried out under both laboratory and field conditions. The results include empirical probability functions for output, delays, and crew-dependent productivity, offering insights into process variability and its impact on performance. Each method is characterized by its theoretical foundations, algorithmic structure, and application areas. The models have been validated through statistical tests and operational field data and can be applied as decision-support tools in both scientific research and industrial management. Given the extensive nature of the described methods, the article provides a comprehensive reference list for readers interested in further exploration and practical implementation in mining engineering.

The article addresses the problem of optimising a selected production process in a company from the refractory products industry. As part of the research, individual activities were divided, identifying key wastes occurring in the production process. In addition, the 5S (the 5S methodology—Sort, Set in Order, Shine, Standardise, and Sustain) quality system was modified, its efficiency was increased, and a better work organisation was established based on it. Data from the actual production process were analysed based on total work efficiency using the OEE (Overall Equipment Effectiveness) coefficient. The use of machine working time was indicated, and key parameters were determined, i.e., availability, efficiency, and quality of the implemented production processes. The results obtained in the course of the research were compared to the Word Class OEE standards. The goal of the work is to indicate possibilities and recommendations for increasing production efficiency without increasing costs, thanks to actions reducing the number of production defects and optimal distribution of employees on the production line. The presented analyses can help assess the management processes of other manufacturing companies operating in this highly specialised manufacturing sector. At the same time, the research conclusions enable other entities to evaluate the implementation of the proposed solutions in practice without incurring unnecessary financial outlays on improving production processes.

Manufacturing companies strive to minimize costs, maximize efficiency and improve production quality, which is crucial for market competitiveness. As companies grow and technologies evolve, increasingly complex challenges arise in effectively managing and improving production processes. One of the tools that helps companies improve their processes is value-stream mapping (VSM). The article focuses on the use of VSM in the production process of hand tools used in the construction industry. The paper presents selected aspects of the optimization of the production process using the mapping concept. The research identified and characterized the most important processes occurring in the production of hand tools used in construction. Then, basic data on the value stream was collected and the need for improvements and actions aimed at optimizing the value stream was indicated. Financial results, key performance indicators (KPIs), machine operation and reliability, energy consumption in the production process and overall equipment effectiveness (OEE) before and after improvements were calculated. The analysis carried out allowed for the optimization of the production process in terms of economy and energy consumption. As a result of the improvements, the productivity of injection-molding workers increased by 9.4% and the overall equipment efficiency by 18%. The machine availability rate increased from 70.3% to 85.2%. After implementing the improvements, the company is able to save approximately 295,488 kWh annually, i.e., approximately EUR 53,253, while 1 kWh currently costs producers in Poland EUR 0.18. The conclusions and results described in the paper constitute a solid basis for further development of an improvement project for the selected company.

The entire ecology is obviously being significantly impacted by climate change. Its causes must be found and addressed before it can be prevented. Therefore, this research investigates the impact of Green Production Processes (GRPP), Technological Globalization (TGLO), Renewable Energy Consumption (RECN) and Gross Domestic Product (GDP) on ECOF (Ecological Footprint) in Turkey from 1990 to 2022 using the Autoregressive Distributed Lag (ARDL) and Frequency Domain Causality methods. The E-Views 12 statistical software was used for the ARDL analysis, while the STATA 17 software was used for the Frequency Domain Causality. The ARDL outcome in the long run showed that GRPP and GDP contribute to ECOF significantly, while TGLO and RECN reduce ECOF insignificantly. The implication of this is that GRPP and GDP lead to ecological degradation, while TGLO and RECN contribute to ecological quality negligibly. In the short run, TGLO reduces ECOF, while GDP increases ECOF. This means that TGLO drives ecological quality, while GDP reduces it. Furthermore, the outcome of the Frequency Domain Causality confirms that GRPP and TGLO Granger-cause ECOF in the short, medium and long term. RECN, on the other hand, only Granger-causes ECOF in the long run, while there is no causal relationship between GDP and ECOF. This study recommends stringent environmental policies and investments in clean energy technologies, such as renewable energy.