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- MakaleAçık Erişim2026Scopus SJRQ1A Hybrid Deep Learning–Fuzzy–Genetic Framework for Climate-Resilient Agricultural Investment and Resource Allocation Under Carbon Market Uncertainty
Erdogdu, Aylin; Dayi, Faruk; Yildiz, Ferah; Esmer, Yusuf; Ganji, Farshad
Climate variability, environmental uncertainty, and carbon-market dynamics increasingly challenge agricultural investment and resource allocation decisions worldwide. This study proposes an integrated hybrid decision-support framework combining Long Short-Term Memory (LSTM) deep learning, Interval Type-2 Fuzzy Logic Systems, and Genetic Algorithms to support climate-resilient agricultural investment analysis under uncertainty. The framework integrates predictive modeling, uncertainty representation, and multi-objective optimization within a unified computational architecture. The empirical analysis was conducted using agricultural, climate, and carbon-market datasets covering Europe, Asia, and Africa over the 2010–2025 period. Agricultural productivity indicators, commodity price variables, climate-risk parameters, and carbon-market data were integrated into the modeling process. LSTM models were employed to analyze temporal agricultural and climate-related dynamics, while Interval Type-2 fuzzy systems were used to represent ambiguity associated with environmental and market uncertainty. Genetic Algorithms were subsequently applied to optimize investment allocation under conflicting objectives related to profitability, sustainability, and risk. The findings suggest that the proposed hybrid framework may improve adaptive investment evaluation and optimization performance under uncertain climate conditions relative to standalone computational approaches within the scope of the analyzed datasets. The results further highlight the importance of integrating predictive analytics, uncertainty modeling, and sustainability-oriented optimization within agricultural decision-support systems. However, the framework should be interpreted as a climate-resilient decision-support architecture rather than a universally deterministic forecasting mechanism. Overall, the study contributes to the emerging literature on agricultural sustainability and climate-resilient investment by presenting a transparent and uncertainty-aware computational framework under evolving environmental and carbon-market conditions.
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İncele - MakaleAçık Erişim2026Scopus SJRQ1A comparative study of metaheuristic algorithms for thermoeconomic optimization of an Organic Rankine Cycle
Tayfur, Bilal; Turhal, Uğur; Tozlu, Alperen
The efficient recovery of waste heat from biogas power plants via Organic Rankine Cycles (ORC) is essential for sustainable energy but requires satisfying conflicting thermodynamic and economic objectives. This study addresses the need for robust optimization strategies by evaluating nine recent metaheuristic algorithms to identify the most effective methods for maximizing net power while minimizing system costs. A multi-objective thermoeconomic model utilizing the specific exergy costing method was developed for a toluene-based ORC and optimized using algorithms including Dung Beetle, Snake, and NSGA-II. The optimization reduced the unit electricity cost by 9.1% (from $0.088/kWh to $0.080/kWh), with the Dung Beetle algorithm exhibiting superior convergence (Hypervolume 25.90) and the Snake algorithm demonstrating the best solution uniformity (Spread 1.54) with computational speed (0.084 s). These findings underscore that selecting algorithms based on specific exploration-exploitation characteristics is critical for achieving cost-effective and thermodynamically efficient power generation designs.
AtıfOpenAlex2İncele - MakaleAçık Erişim2022Scopus SJRQ3A Two-Stage ORC Integration to an Existing Fluidized Bed Sewage Sludge Incineration Plant for Power Production in the Scope of Waste-to-Energy
Abusoglu, A.; Tozlu, A.; Anvari-Moghaddam, A.
This paper presents the design, evaluation, and optimization of an electricity generation system based on the two-stage organic Rankine cycle (TS-ORC), which utilizes the waste heat of an existing fluidized bed sewage sludge incineration (FBSSI) facility. The facility incinerates an average of 300 tons per day of sewage sludge with a dry matter content of 22%. After the drying process, the sewage sludge is burned in a fluidized bed combustor, and exhaust gas at a temperature of about 850-900ºC is released due to the combustion. The system provides the energy required to dry the sludge from this exhaust gas. In this study, a TS-ORC is designed to be coupled to the exhaust gas flowlines discharged to the atmosphere at two different points in the FBSSI plant. The exergy efficiency of the FBSSI facility is found to be 70.5%. Three different working fluids are selected to examine the variations of thermodynamic and thermoeconomic performance parameters of the designed TS-ORC system. The highest power generation in the TS-ORC system (183.40 kW) is achieved using R1234yf as working fluid. R1234yf is also the most expensive fluid for electricity generation among the other working fluids (10.57 $/h). The least electricity generation in the TS-ORC (142.70 kW) occurs at the thermoeconomically most affordable cost with R245fa (9.35 $/h).
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