Biomass energy is abundant, clean, and carbon dioxide neutral, making it a viable alternative to fossil fuels in the near future. The release of syngas from biomass thermochemical treatments is particularly appealing since it may be used in a variety of heat and power generation systems. When a syngas with low tar and contaminants is required, downdraft gasifiers are usually one of the first gasification devices deployed. It is time-consuming and impractical to evaluate a gasification system's performance under multiple parameters, using every type of biomass currently available, which makes rapid simulation techniques with well-developed mathematical models necessary for the efficient and economical use of energy resources. This work attempts to examine, through model and experimentation, how well a throated downdraft gasification system performs when using pretreatment biomass feedstock that has been characterized. For the analyses, peanut shell (PS), a biomass waste easily obtained locally, was used. The producer gas generated with 9 mm PS pellets had a composition of 17.93% H2, 24.43 % CO, 12.47 % CO2, and 1.22% CH4 on a wet basis at the value of 0.3 equivalency ratio and 800 °C gasification temperature. The calorific value was found to be 4.96 MJ/Nm3. The biomass feedstock PS is found to be suitable for biomass gasification in order to produce syngas.

Project risk management in the mining industry is necessary to identify, analyze and reduce uncertainty. The engineering features of mining enterprises, by their nature, require improved risk management tools. This article proves the relevance of creating a simulation model of the production process to reduce uncertainty when making investment decisions. The purpose of the study is to develop an algorithm for deciding on the economic feasibility of creating a simulation experiment. At the same time, the features and patterns of the cases for which the simulation experiment was carried out were studied. Criteria for feasibility assessment of the model introduction based on a qualitative parameters became the central idea for algorithm. The relevance of the formulated algorithm was verified by creating a simulation model of a potassium salt deposit with subsequent optimization of the production process parameters. According to the results of the experiment, the damage from the occurrence of a risk situations was estimated as a decrease in conveyor productivity by 32.6%. The proposed methods made it possible to minimize this risk of stops in the conveyor network and assess the lack of income due to the risk occurrences.

This study investigates the impact of perceived innovative leadership on team innovation performance, with innovation climate acting as a mediating variable. A quantitative research approach, including a survey of team members across various industries, was used to collect data. Analysis through Structural Equation Modeling (SEM) reveals that perceived innovative leadership significantly positively influences team innovation performance, with innovation climate partially mediating this relationship. The findings emphasize the critical role of innovative leadership and a positive innovation climate in fostering organizational innovation, offering valuable insights for management practices. This paper also discusses the study’s limitations and provides directions for future research.

In the context of a globalized economic environment, businesses are facing an increasing number of environmental challenges, prompting them not only to pursue economic benefits but also to focus on environmental protection and social responsibility. Green supply chain management (GSCM) and green innovation have become key strategies for enterprises aiming for sustainable development. This study explores the impact of green supply chain practices on green innovation performance, with a focus on how knowledge management and organizational integration serve as mediating variables in this relationship. Grounded in the resource-based view (RBV) and knowledge-based view (KBV) theories, this research employs surveys and in-depth interviews with companies across various industries, combined with the analysis of structural equation modeling, to reveal the complex relationship between GSCM practices, knowledge management capabilities, levels of organizational integration, and green innovation performance. The results show that GSCM practices significantly enhance corporate green innovation performance through effective knowledge management and organizational integration. These findings enrich the theories of GSCM and green innovation, providing practical guidance for enterprises on how to enhance green innovation performance through strengthening knowledge management and organizational integration. Finally, this study discusses its limitations and suggests possible directions for future research, such as exploring the differences in findings across different industry backgrounds and examining other potential mediating or moderating variables.

This investigation extends into the intricate fabric of customer-based corporate reputation within the banking industry, applying advanced analytics to decipher the nuances of customer perceptions. By integrating structural equation modeling, particularly through SmartPLS4, we thoroughly examine the interrelations of perceived quality, competence, likeability, and trust, and how they culminate in customer satisfaction and loyalty. Our comprehensive dataset is drawn from a varied demographic of banking consumers, ensuring a holistic view of the sector’s reputation dynamics. The research reveals the profound influence of these constructs on customer decision-making, with likeability emerging as a critical driver of satisfaction and allegiance to the bank. We also rigorously test our model’s internal consistency and convergent validity, establishing its reliability and robustness. While the direct involvement of Business Intelligence (BI) tools in the research design may not be overtly articulated, the analytical techniques and data-driven approach at the core of our methodology are synonymous with BI’s capabilities. The insights garnered from our analysis have direct implications for data-driven decision-making in banking. They inform strategies that could include enhancing service personalization, refining reputation management, and improving customer retention efforts. We acknowledge the need to more explicitly detail the role of BI within the research process. BI’s latent presence is inherent in the analytical processes employed to interpret complex data and generate actionable insights, which are crucial for crafting targeted marketing strategies. In summary, our research not only contributes to academic discourse on marketing and customer perception but also implicitly demonstrates the value that BI methodologies bring to understanding and influencing consumer behavior in the banking sector. It is this blend of analytics and marketing intelligence that equips banks with the strategic leverage necessary to thrive in today’s competitive financial landscape.

A method for studying the resilience of energy and socio-ecological systems is considered; it integrates approaches developed at the International Institute of Applied Systems Analysis and the Melentyev Institute of Energy Systems (MESI) of the Siberian Branch of the Russian Academy of Sciences. The article discusses in detail the methods of using intelligent information technologies, in particular semantic technologies and knowledge engineering (cognitive probabilistic modeling), which the authors propose to use in assessing the risks of natural and man-made threats to the resilience of the energy sector and social and ecological systems. More attention is paid to the study and adaptation of the integral indicator of quality of life, which makes it possible to combine these interdisciplinary studies.