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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.

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.

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 biodiesel production process is extensively studied in the literature, focusing on mechanisms, modeling, and economic aspects, yet plant design and fluid flow losses remain underexplored areas. The study addressed this gap by designing a biodiesel production plant, analyzing flow losses, and developing a pipe network and suitable pump models. In this study, an integration of biodiesel production plant design and simulation of continuous production of Calophyllum inophyllum biodiesel was investigated. Biodiesel production encompasses complex stages that involve systematic planning and system design. The goal of the plant design is to reduce the losses that occur during the conversion process, which can reduce the capital cost of the plant. A few assumptions were made when selecting biodiesel plant materials, such as pipes, pumps, fittings, and bends. These assumptions were based on considerations of the biodiesel fluid properties and pressure requirements. On the other hand, Aspen Plus was used to simulate the biodiesel production process. Calophyllum inophyllum was considered oil as the biodiesel feedstock and was inputted to the Aspen Plus as triglyceride composition. The simulation was carried out with rigorous kinetic reactions using the Non-Random Two-Liquid (NRTL) method to predict the liquid equilibrium in the reactor. Results revealed that the designed steel pipe meets safety requirements with a bursting pressure of 49.68MPa, capable of withstanding the maximum pressure of 4 bar and turbulent flow conditions. Additionally, the selected pump satisfies the required head and flow rate, ensuring efficient fluid movement. Moreover, simulation results closely matched experimental data, and 88% of biodiesel yield was recorded.

Production process outsourcing not only enhances farmers’ operation capability but also contributes to income growth. Utilizing field survey data from five provinces—Inner Mongolia, Gansu, Ningxia, Henan, and Shaanxi—this study employs an endogenous switching regression model to analyze the impact of production process outsourcing on the enhancement of farmers’ operation capability and the income-enhancing effect. The results reveal the following: (1) Production process outsourcing significantly improves farmers’ operation capability and increases income. (2) A higher degree of adoption of production process outsourcing correlates with greater improvements in farmers’ operation capability. (3) The impact of production process outsourcing on farmers’ operation capability varies with individual endowments; farmers with higher education levels, a larger number of laborers, and smaller planting areas experience more pronounced improvements in management capabilities when participating in outsourcing. (4) Production process outsourcing partially mediates the income-enhancing effect through its influence on farmers’ operation capability. To further promote income growth, it is essential to enhance the agricultural outsourcing market supply system, expand farmers’ access to production service information, and prioritize the development of farmers’ operation capability.