In the contemporary era, marked by the increasing significance of sustainable energy sources, biomass gasification emerges as a highly promising technology for converting organic materials into valuable fuel, offering...
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In the contemporary era, marked by the increasing significance of sustainable energy sources, biomass gasification emerges as a highly promising technology for converting organic materials into valuable fuel, offering an environmentally friendly approach that not only mitigates waste but also addresses the growing energy demands. However, the effectiveness of biomass gasification is intricately tied to its predictability and efficiency, presenting a substantial challenge in achieving optimal operational parameters for this complex process. It is at this precise juncture that machine learning assumes a pivotal role, initiating a transformative paradigm shift in the approach to biomass gasification. This article delves into the convergence of machine learning and the prediction of biomass gasification and introduces two innovative hybrid models that amalgamate the Support Vector Regression (SVR) algorithm with Coot optimizationalgorithm (COA) and walrus optimization algorithm (WaOA). These models harness nearby biomass data to forecast the elemental compositions of CH4 and C2Hn, thereby enhancing the precision and practicality of biomass gasification predictions, offering potential solutions to the intricate challenges within the domain. The SVWO model (SVR optimized with WaOA) is an effective tool for predicting these elemental compositions. SVWO exhibited outstanding performance with notable R2 values of 0.992 for CH4 and 0.994 for C2Hn, emphasizing its exceptional accuracy. Additionally, the minimal RMSE values of 0.317 for CH4 and 0.136 for C2Hn underscore the precision of SVWO. This accuracy in SVWO's predictions affirms its suitability for practical, real-world applications.
Transport energy demand (TED) forecasting is a crucial issue for countries like Turkey that are dependent on external resources. The accuracy and effectiveness of these forecasts are extremely important, especially fo...
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Transport energy demand (TED) forecasting is a crucial issue for countries like Turkey that are dependent on external resources. The accuracy and effectiveness of these forecasts are extremely important, especially for the strategies and plans to be developed. With this in mind, different forms of forecasting models were developed in the present study using the walrus Optimizer (WO) and White Shark Optimizer (WSO) algorithms to estimate Turkey's energy consumption related to road and railway transportation modes. Additionally, another objective of this study was to examine the impacts of different transport modes on energy demand. To investigate the effect of demand distribution among transport modes on energy consumption, model parameters such as passenger-kilometers (P-km), freight-kilometers (F-km), carbon dioxide emissions (CO2), gross domestic product (GDP), and population (POP) were utilized in the development of the models. It was found that the WO algorithm outperformed the WSO algorithm and was the most suitable method for energy demand forecasting. All the developed models demonstrated a better performance level than those reported in previous studies, with the best performance achieved by the semi-quadratic model developed with the WO, showing a 0.95% MAPE value. Projections for energy demand up to the year 2035 were established based on two different scenarios: the current demand distribution among transport modes, and a demand shift from road to rail transportation. It is anticipated that the proposed energy demand models will serve as an important guide for effective planning and strategy development. Moreover, the findings suggest that a balanced distribution among transport modes will have a positive impact on transport energy and will result in lower energy requirements.
In a cloud computing environment, tasks are divided among virtual machines (VMs) with different start times, duration and execution periods. Thus, distributing these loads among the virtual machines is crucial, in ord...
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In a cloud computing environment, tasks are divided among virtual machines (VMs) with different start times, duration and execution periods. Thus, distributing these loads among the virtual machines is crucial, in order to maximize resource utilization and enhance system performance, load balancing must be implemented that ensures balance across all virtual machines (VMs). In the proposed framework, a credit-based resource-aware load balancing scheduling algorithm (HO-CB-RALB-SA) was created using a hybrid walrus optimization algorithm (WOA) and Lyrebird optimizationalgorithm (LOA) for cloud computing. The proposed model is developed by jointly performing both load balancing and task scheduling. This article improves the credit-based load-balancing ideas by integrating a resource-aware strategy and scheduling algorithm. It maintains a balanced system load by evaluating the load as well as processing capacity of every VM through the use of a resource-aware load balancing algorithm. This method functions primarily on two stages which include scheduling dependent on the VM's processing power. By employing supply and demand criteria to determine which VM has the least amount of load to map jobs or redistribute jobs from overloaded to underloaded VM. For efficient resource management and equitable task distribution among VM, the load balancing method makes use of a resource-aware optimizationalgorithm. After that, the credit-based scheduling algorithm weights the tasks and applies intelligent resource mapping that considers the computational capacity and demand of each resource. The FILL and SPILL functions in Resource Aware and Load utilize the hybrid optimizationalgorithm to facilitate this mapping. The user tasks are scheduled in a queued based on the length of the task using the FILL and SPILL scheduler algorithm. This algorithm functions with the assistance of the PEFT approach. The optimal threshold values for each VM are selected by evaluating the task based on
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