Reduction In Operating Costs Research Articles

Analyzing financial analysts' role in business optimization and advanced data analytics

This paper explores the evolving role of financial analysts in business optimization, focusing on their increasing reliance on advanced data analytics to drive strategic decision-making. It examines the historical shift from traditional financial analysis to more data-centric responsibilities, highlighting how modern financial analysts contribute to operational efficiency, cost reduction, and revenue growth through data-driven insights. Integrating advanced tools such as artificial intelligence, machine learning, and predictive analytics has enabled financial analysts to interpret complex datasets, optimize business processes, and improve market positioning. However, adopting these technologies comes with challenges, including a steep learning curve, data integration difficulties, and ethical concerns related to data privacy and bias in AI algorithms. This paper also discusses future trends in financial analysis, emphasizing the growing importance of real-time data analysis and automation in business optimization. The paper concludes by stressing the need for financial analysts to continually adapt to the rapidly evolving technological landscape to effectively shape corporate strategy.

Secure low-cost photovoltaic monitoring system based on LoRaWAN network and artificial intelligence

The advanced study will focus on developing a secure and cost-effective photovoltaic monitoring system using Long Range Wide Area Network (LoRaWAN) and Artificial Intelligence technologies for highly risky missions. With the world shifting to using renewable energy, the development of secure and efficient cost-effective monitoring systems for photovoltaic installations becomes very important. However, most of the solutions in use for PV installation monitoring are characterized by high costs, hence their limited use, especially in developing regions. In this regard, the present work presents country-specific protocols designed for the country of Turkey. The proposed system in this study is expected to offer real-time monitoring and data collection at a very meager cost by utilizing inexpensive microcontrollers and sensors. The accuracy rate demonstrated by the research is highly above board: 97.65%. It costs $210, with a very low power consumption of 0.5W and a range of 10 km. The major features underpin real-time monitoring with wide area coverage for effectiveness and efficiency in PVs monitoring. Test results in many places around Turkey have turned out reliable and accurate to justify a possible reduction in operational costs without compromising the quality of data received. This design is reinforced by a detailed mathematical model that assures one of its thorough analysis in performance. There is custom-built firmware that enhances communication efficiency and ensures data integrity in implementation. Comparative studies carried out with existing technologies indicate that the proposed system is cost-effective and operationally sound. This work gives a scalable, low-cost solution that probably might raise the uptake of PV systems in economically constrained areas.

Optimizing energy hub systems: A comprehensive analysis of integration, efficiency, and sustainability

This study introduces a novel application of modified particle swarm optimization (PSO) for optimizing multi-energy hub systems (EHSs) to enhance efficiency and sustainability. The proposed method leverages PSO to optimize the scheduling of various energy resources, including gas turbines, biomass units, and renewable sources such as solar and wind power. Unlike traditional optimization approaches that rely on genetic algorithm (GA) and complex encoding schemes, the PSO algorithm simplifies the process using real-valued vectors and direct communication within the swarm, which significantly reduces implementation complexity. Key contributions of this work include the development of a tailored PSO algorithm that integrates seamlessly with the multi-objective optimization of EHSs. The algorithm simultaneously targets a reduction in operational costs and carbon emissions, offering a comprehensive solution for energy hub design. The proposed PSO approach has demonstrated a 10.35 % reduction in operating costs and an 85.03 % decrease in CO2 emissions compared to traditional baseline setups. In comparative analysis, the integration of renewable sources using the PSO algorithm resulted in a 77.91 % reduction in total CO2 emissions and an 85.61 % decrease in operating costs, showcasing its effectiveness in advancing both economic and environmental objectives. Furthermore, the study provides a detailed evaluation of various scenarios, revealing that the PSO-optimized EHS configuration achieves a significant reduction in reliance on non-renewable energy sources (RES). For instance, the incorporation of photovoltaics and wind turbines in the EHS setup led to a 46.39 % increase in energy sold to the grid and a 26.82 % decrease in electricity purchased from external sources. These quantitative results underscore the robustness and practical benefits of the proposed PSO method in designing and optimizing energy systems for improved sustainability and cost-effectiveness.

The Critical Role of SREs in Safeguarding Healthcare Data Integrity and Availability

This article explores the critical role of Site Reliability Engineers (SREs) in the rapidly evolving landscape of healthcare technology. As healthcare organizations increasingly adopt digital solutions, SREs have become indispensable in ensuring data integrity, system availability, and regulatory compliance. The article discusses the unique challenges faced by SREs in healthcare environments, including managing vast amounts of sensitive patient data, implementing robust security measures, and maintaining high system reliability. It examines the key responsibilities of SREs, such as proactive monitoring, incident response, data consistency management, and security compliance. Furthermore, the article highlights the significant impact of SRE practices on healthcare delivery, including enhanced patient care, improved operational efficiency, risk mitigation, and cost reduction. Through a combination of statistical data and industry insights, this work underscores the growing importance of SREs in shaping the future of healthcare technology and improving overall patient outcomes.

A two-layer strategy for sustainable energy management of microgrid clusters with embedded energy storage system and demand-side flexibility provision

The intermittent nature of renewable energy generation and the fluctuating demands pose persistent challenges in microgrid operations. In response, stakeholders and operators have turned to clustering the geographically adjacent microgrids as a solution. In this context, this paper introduces a novel two-layer energy management strategy for microgrid clusters, utilizing demand-side flexibility and the capabilities of shared battery energy storage (SBES) to minimize operational costs and emissions, while ensuring a spinning reserve within individual microgrids to prevent load-shedding. In the lower layer, the proposed approach devises optimal day-ahead operation policies, while the upper layer employs a cooperative strategy to further optimize the operational efficiency across the entire cluster. The energy management problem is accurately formulated as a mixed integer quadratic programming (MIQP) optimization, which incorporates linear terms in the problem's constraints. The formulation accounts for operational costs associated with SBES including expenses of charging/discharging and changes in operating states (CiOS). Real-world case studies with a cluster of three microgrids in Australia validate the effectiveness of this approach. Results show a reduction in operational costs for the base case scenario by 6.96 % compared to conventional microgrid management strategies. Sensitivity analyses further demonstrate the economic benefits of varying SBES capacity and flexibility pricing, with savings ranging from 6.5 % to 8.1 %. The proposed strategy also reduces CO2 emissions by up to 11.6 %, while improving system reliability. This strategy holds promise for integration into distributed energy systems with high renewable penetration and clustered local grids, offering significant advantages for utility operators and end-users through improved energy efficiency and reduced emissions.

Evaluation of Selected Factors Affecting the Speed of Drivers at Signal-Controlled Intersections in Poland

In traffic engineering, vehicle speed is a critical determinant of both the risk and severity of road crashes, a fact that holds particularly important for signalized intersections. Accurately selecting vehicle speeds is crucial not only for minimizing accident risks but also for ensuring the proper calculation of intergreen times, which directly influences the efficiency and safety of traffic flow. Traditionally, the design of signal programs relies on fixed speed parameters, such as the posted speed limit or the operational speed, typically represented by the 85th percentile speed from speed distribution data. Furthermore, many design guidelines allow for the selection of these critical speed values based on the designer’s own experience. However, such practices may lead to discrepancies in intergreen time calculations, potentially compromising safety and efficiency at intersections. Our research underscores the substantial variability in the speeds of passenger vehicles traveling intersections under free-flow conditions. This study encompassed numerous intersections with the highest number of accidents, using unmanned aerial vehicles to conduct surveys in three Polish cities: Toruń, Bydgoszcz, and Warsaw. The captured video footage of vehicle movements at predetermined measurement sections was analyzed to find appropriate speeds for various travel maneuvers through these sections, encompassing straight-through, left-turn, and right-turn relations. Our analysis focused on how specific infrastructure-related factors influence driver behavior. The following were evaluated: intersection type, traffic organization, approach lane width, number of lanes, longitudinal road gradient, trams or pedestrian or bicycle crossing presence, and even roadside obstacles such as buildings, barriers or trees, and others. The results reveal that these factors significantly affect drivers’ speed choices, particularly in turning maneuvers. Furthermore, it was observed that the average speeds chosen by drivers at signalized intersections did not reach the permissible speed limit of 50 km/h as established in typical Polish urban areas. A key outcome of our analysis is the recommendation for a more precise speed model that contributes to the design of signal programs, enhancing road safety, and aligning with sustainable transport development policies. Based on our statistical analyses, we propose adopting a more sophisticated model to determine actual vehicle speeds more accurately. It was proved that, using the developed model, the results of calculating the intergreen times are statistically significantly higher. This recommendation is particularly pertinent to the design of signal programs. Furthermore, by improving speed accuracy values in intergreen calculation models with a clear impact on increasing road safety, we anticipate reductions in operational costs for the transportation system, which will contribute to both economic and environmental goals.

Thermal Integration in Sugar Production Using Pinch Analysis

Objective: the objective of this study is to conceptualize a network of heat exchangers designed to minimize energy waste and enhance the overall efficiency of the sugar production system.Methods: a systematic approach was adopted to analyze energy flows within the plant, identifying key areas for improvement, particularly in heating and evaporation processes. Heat accumulations in cascades and graphical analyses of composite curves were developed using specialized software to optimize heat exchange.Results: the results indicate a significant potential for energy savings, reducing the consumption of cooling and heating utilities in the plant by 7% and 30%, respectively. The developed computational tool allows for energy integration from simple processes to those with hundreds of streams. The pinch technology concept estimated an annual total savings of $464,850.08 in the selected process.Conclusion: this study demonstrates that thermal integration through pinch analysis not only improves energy efficiency in the sugar industry but also contributes to a considerable reduction in operational costs and environmental impact, providing a valuable tool for the industry’s sustainability and competitiveness.

Operational electricity cost reduction using real-time simulators

Ensuring the safe and cost-effective electricity transmission to consumers, along with the efficient and sustainable operation of power systems, has long been a primary objective for power system managers and operators. However, achieving optimal performance in a modern power system requires timely planning and operational optimal routines to be run within the realm of real-time simulation programs, and sometimes, the divergence becomes a problem with these developed routines. Despite extensive research aimed at advancing these objectives, the growing complexity of networks and their constraints has often led to the oversight of simultaneously considering both technical and economic aspects during operation. In this paper, an innovative real-time framework is introduced to reduce the operational costs of a power system swiftly. This framework addresses various nonlinear constraints, such as transmission losses, generation-consumption balance, power device limitations, and transient stability constraints, across various operational scenarios within a real-time simulator. This novel approach leverages scattered search and intentional contingencies within the network to pinpoint the optimal operating point, taking into account various network dynamics, including Automatic Voltage Regulators (AVR), governors, and tap of the transformers. The proposed method offers a fast and robust approach to identifying the most effective operating conditions for a power system. It incorporates considerations for sudden contingencies and extends capabilities through parallel simulations. It not only facilitates pre-determined preventive actions but also enables the adjustment of control parameters during post-contingency periods, such as fine-tuning of the active and reactive power generation of generators and adjusting the tap settings of transformers within power networks. Since the network simulation can be executed on distributed computers, referred to as ’Global,’ it is possible to achieve global network optimization. This approach allows for the consideration of grid networks, including renewable energy sources with models distributed through PCs. Also, it accounts for the induction motor losses within all factories involved in the global simulation. The results obtained from simulating the proposed method on a commercial real-time simulator demonstrate the superior effectiveness of the proposed framework compared to the existing methodologies, highlighting its potential to enhance the operational efficiency and economic viability of power systems.

Correction: Performance Metrics of the Newly Introduced Britannia Silver Process: Significant Reductions in Operating Costs, Energy Usage, and Scope 1 Carbon Emissions in the Industrial-Scale Refining of Silver

Correction: Performance Metrics of the Newly Introduced Britannia Silver Process: Significant Reductions in Operating Costs, Energy Usage, and Scope 1 Carbon Emissions in the Industrial-Scale Refining of Silver

Optimizing reservoir characterization: insights from integrated data analysis

The Onshore Hydrocarbon prospectivity of the Niger Delta X-field is examined through the integration of 3D seismic and recorded information from well log data. Probable reservoirs of hydrocarbon-bearing were delineated to tackle the non-uniqueness in the identification of hydrocarbon quantity of concern. Three significant faulting patterns or systems were delineated and their architectural attributes depict a typical Niger Delta embedded anticlinal structural pattern. The study field exhibited both synthetic and antithetic structures, hence only fault on delineated reservoirs was used for structural modeling. All well locations were sited within the fault-supported synthetic and anticlinal structures and the static characteristics within the well coordinates were analyzed through petrophysical assessment. Depicted reservoir sands show low gamma ray readings, low volume of shale, considerate hydrocarbon saturation and low water saturation. The established petrophysical models showed a better net-to-gross (NTG) ratio, for the entire delineated reservoir units. This has contributed to the assessment of hydrocarbon-bearing reservoir units before employing the strategy for field development scenario, in order to eliminate dry hole drilling campaign. These will contribute to the reduction of operational costs, having delineated the accurate geometry and petrophysical model of hydrocarbon reservoir units.

Reliability-constrained configuration optimization for integrated power and natural gas energy systems: A stochastic approach

With the escalating dependence on electricity and natural gas infrastructure, ensuring both reliability and economic efficiency becomes paramount. It necessitates reliability centric measures to mitigate disruptions that could cascade between these interconnected systems. To address this challenges, this paper introduces a reliability-constrained two-stage stochastic model to optimize power-to-gas (P2 G) and gas-to-power (G2P) unit placement and sizing, aiming to enhance the reliability of both systems under stochastic scenarios. The proposed model, employing Sequential Monte Carlo (SMC) within its optimization framework, seeks to minimize investment, operation, and reliability costs. By addressing temporal uncertainties in component outages for both systems and considering uncertainties in power and gas system loads with a high temporal resolution and annual load growth, the model provides a comprehensive reliability perspective. Furthermore, sensitivity analysis is conducted to explore the impact of varying Values of Lost Load (VOLL) on the planning results. Numerical evaluation, using two integrated energy systems including IEEE 14-bus-10-gas node, and large-scale energy systems including IEEE 118-bus-85-gas node integrated power-gas system (IPGS), demonstrates a significant 12.53 % improvement in overall system reliability. Furthermore, a 2.81 % reduction in operation costs and a substantial 26.3 % reduction in reliability costs, validating the effectiveness of the proposed model.

Stochastic two-stage multi-objective unit commitment of distributed resource energy systems considering uncertainties and unit failures

Compared to centralized generation technology, distributed energy resource systems are susceptible to energy risks caused by boundary uncertainties and unit failures. This study introduces a stochastic two-stage multi-objective optimization method to address reliability-based unit commitment issues. In the day-ahead stage, operational state and reserve capacity are determined to minimize prescheduled operation costs based on forecasted parameters. In the real-time stage, a decision-dependent stochastic reliability method is proposed to simulate outage scenarios. Reserve resources within available units are allocated to mitigate forecasting errors and unit failures. Additionally, the grid interaction ratio and penalty cost are added to restrict the depth and frequency access to the grid. Four comparative cases analyze the effects of the proposed methodology. This method innovatively achieves the simulation of stochastic multi-unit outages and delete faulty units in the operation scheme. The optimal results show that the risks of electricity and cooling supply are underestimated, while the risks of heating are overestimated, compared to N-1 reliability. Furthermore, Pareto analysis of the multi-objective problem enhances independent operational capacity through utilization of reserve resources. Grid dispatch pressure is reduced since purchased power can be used as day-ahead planning. Thus, the methodology achieves collaborative optimization of reliability with a reduction of operation costs, offering effective guidance for engineering applications.

A new strategy to design the purification process of ethyl tertiary butyl ether gasoline additive via hybrid extractive-reactive distillation

Here we propose a new strategy for designing the purification process of ethyl tertiary butyl ether (ETBE) gasoline additive. Our proposal suggests replacing the last two columns of the triple-column extractive distillation process from previous work with a single reactive distillation column. This column incorporates an ethylene oxide hydration reaction, yielding ethylene glycol, which can be utilized as a solvent for extractive distillation. This replacement is able to reduce the total number of columns, the amount of solvent needed, and significantly decreases total energy consumption up to 32% compared to the original process. Furthermore, it benefits from the redistribution of energy between different steam-grade utilities, enabling significant reduction in operational costs. These advantages result in a 3−22% cost decrease compared to the original process. Overall, the proposed process exhibits significant improvements in energy efficiency and cost reduction with respect to the original process with and without heat integration from previous research.

Optimal cost predictive BMS considering greywater recycling, responsive HVAC, and energy storage

A crucial aspect of a sustainable city is ensuring that energy and water supplies can adequately meet urban demand. With the scarcity of natural resources and growing electricity and water demand, it becomes increasingly important for consumers to manage their resource usage more efficiently. This paper proposes a novel perspective of demand-side management coordination strategy for a building's water-energy nexus to enhance the resilience and efficiency of the overall electricity-water-heating system. The model is formulated to optimally coordinates the onsite greywater recycling system, heating, ventilation, air conditioning (HVAC) loads, and distributed energy generation systems with a bidirectional grid connection in a residential building. All subsystems are controlled by a model predictive controller (MPC) receiving real-time time of use (ToU) pricing from electricity and water utilities. The presented mixed integer linear programming model is verified to meet the customers' demands while reducing the operational costs. Results are compared with benchmark systems lacking the water recycling or energy storage system showing 8.3 % operational cost reduction while reducing potable water consumption by 21.5 %. The effect of increased MPC control horizon is also studied showing reduction in cost with increased horizon. Detailed analysis of the proposed framework computational burden and effect of prediction errors is performed to prove the MPC adaptability and robustness. Testing under increased room size and different user preferences further validate the efficacy of the proposed scheme in reducing the operational costs.

Exergoeconomic optimization of a proposed novel combined solar powered electricity and high-capacity cooling load production system for economical and potent generation via utilization of low-grade waste heat source

A novel system for combined electricity and cooling generation was introduced, integrating Flat Plate Solar Collectors (FPSC), Absorptional Heat Transformer (AHT), Organic Rankine Cycle (ORC), and Absorption Cooling Cycle (ACC) systems to utilize low-grade solar energy. The ability to use low-grade waste heat sources (70 °C–90 °C) via FPSC system for high-capacity integrated cooling and electricity generation in a more economical way, a feature not commonly addressed in conventional systems and previous literature studies, was a key advancement. The need for additional generators, boilers, and high-temperature heat sources was eliminated, resulting in substantial cost savings and a simplified system design. The FPSC-AHT integration, identified as having significant advantages over separate electricity and cooling load production, was comprehensively evaluated for its combined exergoeconomic and environmental benefits in multigeneration system design. The modeling was performed using Engineering Equation Solver (EES) and Transient System Simulation Software (TRNSYS) in Izmir, Turkey, with the aim of achieving the heightened economic efficiency and superior Coefficient of Performance (COP) values without high-temperature waste heat sources. Three configurations were examined, with the third demonstrating superior technoeconomic performance due to the increased thermal efficiency of solar hybrid photovoltaic-thermal (PV-T) systems. The higher cost per unit area in the PV-T system was effectively offset by the substantial electricity consumption, contributing to energy savings. Economic indicators for the third configuration included an initial investment of US$9.91 million, annual operational costs of US$1.29 million, a payback period of 4.2 years, an annual energy cost gain of US$9.25 million, a levelized cost of cooling (LCC) of US$0.014/kWh, and an electricity cost (LCE)of US$0.015/kWh. Through exergy analysis, toluene was identified as the optimal working fluid, revealing a total exergy destruction rate of 13245.46 kW. The performance of the proposed system was tested under different operation conditions, and based on these results, a sensitivity analysis and a comparison with the real-word studies were performed. In comparison to real-world data, the proposed system exhibits superior performance metrics, especially in terms of COP and exergy efficiency values. The optimal configuration, established using single and multiobjective optimization approaches based on exergoeconomic parameters, indicated annual electricity and cooling load production of 40,000 MWh and 300 GWh, respectively. The system’s efficiency in producing 1000 kW of electricity power and 4000 kW of cooling load at a comparable cost to systems generating only one output was highlighted. To determine the technoeconomic performance improvement of the proposed integrated system, the optimal configuration of the novel integrated system was compared to a reference plant for similar-scaled integrated power and cooling generation (UCI Trigeneration Plant). Compared to the UCI Trigeneration Plant, the proposed system demonstrated significant improvements in technoeconomic performance. Specifically, the proposed system achieved a 164.13 % increase in annual electricity production, a 97.38 % increase in annual cooling duty, a 60.36 % reduction in initial investment, a 57 % reduction in annual operational costs, and a 47.5 % reduction in payback period. Additionally, the levelized costs of electricity and cooling were 40 % and 22.22 % lower, respectively. Significantly higher electricity and cooling output highlights the system’s ability to meet demanding energy needs. Lower initial investment and operational costs, coupled with a reduced payback period, make the system financially attractive. Lower levelized costs for electricity and cooling increase the system’s competitiveness and affordability. The innovative integration of technologies provides new insights into the design of multigeneration systems, setting a new benchmark for sustainable energy solutions

Study Evaluates Long-Term Corrosion Risk of Load-Bearing, High-Power ESP Cable

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 34898, “Long-Term Corrosion-Risk Evaluation of Load-Bearing, High-Power Electrical Submersible Pump Cable,” by Serko A. Sarian, SPE, Alkhorayef Petroleum, and Hattan Banjar, SPE, and Chidirim Ejim, SPE, Saudi Aramco, et al. The paper has not been peer reviewed. Copyright 2024 Offshore Technology Conference. _ In this study, a fit-for-purpose electrical submersible pump (ESP) load-bearing power cable was deployed inside the production tubing of a sour oil well for an extended period of service. Project economics required the power-cable metallurgy to survive long-term exposure reliably for up to 15% hydrogen sulfide (H2S) and high-salinity production fluids. Laboratory tests simulating varying sour well and extended galvanic corrosion conditions have been performed to determine the adequacy of the selected armor metallurgy in downhole corrosive environments. Introduction In recent years, various types of load-bearing power cable-deployed ESP (CDESP) systems have been introduced, eliminating the need to install or remove a conventional ESP along with the completion and reducing ESP changeover time by more than 80%. More recently, live-well CDESP systems have been developed and tested to eliminate the need for a well-kill operation. The most-critical and costly component of such a system is the load-bearing power cable. The cable has the dual purpose of conveyance and long-term powering-up of the ESP. In a conventional artificial lift operation, the ESP is conveyed using the production tubing with a non-load-bearing power cable strapped to the outside of the tubing. This operation requires a workover rig to remove and reinstall the entire completion and a killed-well operation that might permanently damage the reservoir. The main advantages of a CDESP system include operational efficiency gain and cost reduction, no reservoir skin damage, and production-loss minimization. However, a few challenges exist with the use of CDESP. These cables must have significantly different structures from commercial oil and gas power cables. These differences are detailed in the complete paper. Cable-Design Requirements CDESP system criteria include multiple-year continued service in adverse oilwell and reservoir conditions such as sour fluids, high gas/oil ratios (GOR) and temperatures, and abrasion caused by solids production. The following product acceptance requirements were set in advance: - Three redeployments or retrievals after the initial deployment - Single deployment continued runtime to meet or exceed the regional conventional ESP system runtime Based on regional statistics, a conventional ESP system single-deployment average run time is 2.5 years. The effective CDESP power-cable service life target then becomes 2.5 years × four total deployments = 10 years, which served as the basis for all cable-associated testing and reliability metrics.

Evaluating the impact of emerging hydro-geophysical technologies on sustainable water resource management

Emerging hydrogeophysical technologies are revolutionizing the management of water resources, providing unprecedented insights and enhancing sustainability practices. This article examines recent advancements in hydrogeophysical methods, focusing on time-lapse electromagnetic techniques, unmanned aerial vehicles (UAVs), and artificial intelligence (AI). Through a comprehensive review of the literature and practical case studies, including notable implementations in Africa, the paper highlights how these innovations improve groundwater monitoring, resource management, and predictive capabilities. Time-lapse electromagnetic methods have achieved up to 1-meter resolutions, significantly enhancing groundwater mapping accuracy (Binley & Kemna, 2005). With their high-resolution remote sensing capabilities, UAVs have facilitated detailed hydrological surveys, while AI-driven analyses optimize groundwater predictions and management strategies (Rennie et al., 2021). The study presents a cost-benefit analysis showing a 20-30% reduction in operational costs and a 40% increase in data accuracy. Challenges such as technical limitations and high initial costs are discussed, alongside potential solutions and prospects. The findings underscore the transformative potential of these technologies for achieving sustainable water resource management, offering actionable insights for policymakers, researchers, and practitioners in the field.

Logistics supply of the eco-industrial parks’ residents

This paper provides a comprehensive analysis of the logistics supply for residents of eco-industrial parks and examines its influence on the competitiveness and sustainability of these parks. Eco-industrial parks, as integral components of circular economy systems, facilitate the efficient use of resources, reduction of environmental impact, and optimization of supply chain operations. The purpose of the paper is to assess the logistics systems within eco-industrial parks, identify the challenges and opportunities for enhancing operational efficiency, and explore how these strategies can be adapted to the Ukrainian context, particularly during the ongoing war. The research draws upon international case studies, focusing on the integration of advanced technologies and green logistics practices in China, Europe, and other regions. The methodology used in this paper combines inductive analysis of literature, comparative analysis, and empirical research based on existing models of logistics management within EIPs. A key part of the analysis involves the evaluation of institutional pressures and their role in promoting sustainable supply chain management. The paper examines how these practices, when applied in a coordinated and efficient manner, help to reduce costs and carbon emissions, while also improving resource-sharing between companies located within eco-industrial parks. The results show that implementing circular supply chain management significantly enhances the performance of companies operating within EIPs. Firms that adopt renewable energy sources and optimize their transportation systems using electric vehicles report a 13-14% reduction in operational costs and carbon emissions. The integration of technologies such as Geographic Information Systems and Multi-Criteria Decision-Making tools has also proven beneficial in the site selection and planning of eco-industrial parks, allowing for the optimization of logistics and resource allocation. In Ukraine, which is currently facing critical challenges due to war, adopting these strategies can strengthen industrial clusters, reduce dependence on external resources, and enhance resilience against economic shocks. The scientific novelty of this research lies in the proposed structure of the logistics supply for eco-industrial parks’ residents. The practical implications of this paper include the application of green logistics technologies, the development of self-sustaining energy networks, and the implementation of closed-loop supply chains that promote resource efficiency. Future research directions should focus on the development of logistics optimization models for Ukrainian eco-industrial parks, particularly in war-torn areas, and the assessment of their long-term economic and environmental impact. Additionally, the paper advocates for the expanded use of renewable energy sources and Geographic Information Systems-based planning tools to further improve logistics processes and reduce the ecological footprint of industrial activities. The results of this paper underscore the importance of inter-firm cooperation and the strategic use of logistics to foster both economic and environmental resilience in eco-industrial settings.

Machine Learning Algorithms for Prediction of Boiler Steam Production

The continuous increase in global electricity demand has resulted in boiler power plants becoming a significant energy source. The production of steam is a principal indicator of boiler efficiency, and the accurate prediction of steam production is paramount importance for the enhancement of boiler efficiency and the reduction of operational costs. In this study employs a boiler dataset with a steam production capacity of 420 tons per hour. A total of 25 independent variables were extracted from the original 39 variables through data processing and feature engineering for the purpose of prediction analysis. Subsequently, 8 machine learning models were used for modeling predictions. Grid search cross-validation was employed in order to optimise the performance of the model. The models were analysed and assessed using the Mean Squared Error (MSE) metrics. The results show that random forest achieves the highest accuracy among the 8 single models. Based on 8 models, New Bagging ensemble model is proposed, which combined predictions from 8 single models, demonstrated the optimal overall fit and the lowest MSE, achieved the purpose of the research. The present study demonstrates the ability to analyse and predict complex industrial systems with machine learning algorithms, and provides insights into the use of machine learning algorithms for industrial big data analytics and Industry 4.0. Further work could explore using larger datasets and deep learning to make predictions more accurate.

The Contribution of Community Informatics in Rural Communities to Development

Community informatics (CI) is a generic term used to refer to Information and Communication Technologies (ICTs) deployed in communities to meet community needs, simplify day-to-day tasks, close digital divides, among other benefits. In practice, it is about the use of information and ICTs in community development efforts to enable and empower community processes so as to achieve community social, economic, cultural and political goals. On the other hand, community development is the promotion of participative democracy, sustainable development, human rights, economic opportunity, equality and social justice, through the organization, education and empowerment of people within their communities. This paper established the contribution of community informatics to the development of rural communities with reference to Kaliro district as a case study area. Using participative case study design and mixed methods research approach, responses from 173 CI users and 33 district workers engaged through questionnaire survey and interviews respectively were analyzed and results were presented quantitatively in tables and qualitatively under themes for interpretation. The study found out that CI contributed significantly to the district's social, economic, cultural, and political development. Towards social development, the CI promoted social connection and networking, ensured quick information flow, and fostered knowledge sharing. Towards economic development, they promoted trade and business, business and economic awareness, efficiency and effectiveness in service delivery, reduction in operational costs, job creation, income generation, and fostered new CI developments for businesses. Towards cultural development, they exported culture, promoted cultural awareness, tourism, and reinforced cultural freedom among the people of Kaliro district. In terms of political development, they had promoted government programs, human rights, provided government information, enabled civic education, eased follow-up on political developments, and fostered political stability in the district. Study findings showed that communities leverage CI and its infrastructure to keep up with information about government programs, health, environment, education, personal development, economic living, empowerment, and democracy, among others in a sense that triggers development. However, there is a general literature gap owing to the novelty of CI concept and the dearth in review evidence pointing out the contribution of CI to development of communities. Keywords: Community informatics; Community development; ICT4D; Kaliro district.