Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Artar, Musa(7)
- Tozlu, Alperen(6)
- Özahi, Emrah(4)
- Daloʇlu, Ayşe T.(3)
- Abuşoğlu, Ayşegül(2)
- Balci, Murat(2)
- Dayi, Faruk(2)
- Erdogdu, Aylin(2)
- Esmer, Yusuf(2)
- Ganji, Farshad(2)
- Yildiz, Ferah(2)
- Abusoglu, Aysegul(1)
- Arslantürk, Cihat(1)
- Bilen, Kadir(1)
- Daloglu, Ayse(1)
- Daloglu, Ayse T.(1)
- Daloğlu, Ayşe T.(1)
- Gundogdu, Omer(1)
- Gündogdu, Omer(1)
- Güngör, Osman(1)
- Malgaca, L.(1)
- Ozahi, Emrah(1)
- Topcu, Muhammed Taha(1)
- Uyar, M.(1)
Yayınlar
- 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 SJRQ1Optimizing Budget Allocation for Digital Health Investments Using Metaheuristic Algorithms: A Cost–Impact Analysis for Public Health Systems
Dayi, Faruk; Erdogdu, Aylin; Esmer, Yusuf; Yildiz, Ferah; Ganji, Farshad
Background: In the era of digital transformation, public health systems increasingly rely on digital technologies to improve accessibility, efficiency, and patient outcomes. However, policymakers face significant challenges in allocating limited resources across competing digital health investments characterized by uncertainty and dynamic impacts. Methods: This study introduces the Adaptive Impact–Cost Optimization Theory (AICOT), a hybrid framework integrating fuzzy logic and genetic algorithms to optimize digital health investment portfolios. The model defines the Investment Priority Score (IPS) as a function of cost, expected impact, and implementation feasibility, enabling structured evaluation under uncertainty. A fuzzy inference system with centroid-based defuzzification is used to convert qualitative assessments into quantitative scores, while optimization techniques identify optimal portfolios across different fiscal scenarios. The empirical analysis covers 15 OECD countries (2018–2024) using publicly available datasets. Sensitivity analyses assess robustness under inflation, cost shocks, and changing system priorities. Results: The findings show that blended investment strategies combining routine digital health tools with pandemic-oriented infrastructures yield the highest resilience-adjusted efficiency. Results remain stable across sensitivity scenarios, with pandemic surveillance consistently ranking as a top priority even under increased cost conditions. The model effectively captures cross-country heterogeneity, demonstrating adaptability to different levels of digital maturity. Conclusions: AICOT provides a transparent and policy-relevant decision-support framework that improves resource allocation efficiency and reduces unnecessary expenditures. These contributions support long-term financial sustainability and align with global health objectives, including Universal Health Coverage and Sustainable Development Goal 3 (Good Health and Well-being).
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İncele - MakaleAçık Erişim2025Scopus SJRQ1Energy, exergy and economic optimization application of integrated dual-loop organic rankine cycle: A case of shopping mall power plant
Topcu, Muhammed Taha; Tozlu, Alperen; Bilen, Kadir
This paper presents a thermoeconomic analysis of the Dual-Loop Organic Rankine Cycle (DORC) integrated into the Erzurum Mall Power Plant (EMPP). The DORC system is designed to utilize waste heat at 425 degrees C and 9 kg/s, producing additional power for the existing plant. The integration of dual-loop ORC systems with different critical temperature fluids into an operational facility is analyzed. High-temperature cycles use working fluids such as Toluene, MDM (octamethylcyclotrisiloxane), n-Nonane, and n-Octane, while refrigerants R236fa, R600a, and R124 are employed in the low-temperature cycles. The designed model demonstrates that up to 11.4 % of the plant's existing capacity can be added as extra power. The Toluene/R600a combination is identified as the best fluid pair, delivering the highest total net power output (653.4 kW), the lowest total cost rate (19.18 $/h), and the highest exergy efficiency (20.18 %). Optimization of the DORC system is performed using the NSGA-II in MATLAB. For the toluene/R600a pair, the optimization results show an increase of 13.52 % in the net power output and decrease of 1.48 % in the total cost rate, which is a significant improvement over the initial results.
AtıfWoS3OpenAlex1İncele - MakaleAçık Erişim2024Scopus SJRQ1District heating based on exhaust gas produced from end-of-life tires in Erzincan: Thermoeconomic analysis and optimization
Güngör, Osman; Tozlu, Alperen; Arslantürk, Cihat; Özahi, Emrah
This paper presents the thermoeconomic analysis and optimization of the Erzincan Waste Tire Recycling and Power Plant (EWTRPP) in Turkey and its waste heat-based district heating (DH) system. A thermoeconomical method, namely the specific exergy costing (SPECO), is applied to allocate cost flows through subcomponents of an actual existing plant. Besides, the obtained thermoeconomic results are improved by using an optimization method, namely non-dominant sorting genetic algorithm (NSGA-II), considering the temperature of the exhaust gas discharged to the atmosphere (T25), the thermal conductivity value of the insulation material (ki) and pipe roughness value (s). The energy and exergy efficiencies of the existing plant are found to be 27.37% and 35.57%, respectively, considering the data of the actual plant. Moreover, the energy and exergy efficiencies of the waste heat-based DH system are found to be 43.08% and 55.99%, respectively. The total cost rate of the overall system is deduced 13.98 $/hby means of SPECO method. According to the results of the NSGA-II optimization method, the exergy efficiency and total cost rate of the overall system are evaluated as 6.93% and −2.51 $/h, respectively. Furthermore, it is also proposed that 1304 dwellings can be heated owing to the DH system utilizing the existing exhaust gas.
AtıfScopus9WoS8OpenAlex7İncele - MakaleAçık Erişim2023Scopus SJRQ1Implementation of Active and Passive Vibration Control of Flexible Smart Composite Manipulators with Genetic Algorithm
Uyar, M.; Malgaca, L.
Endpoint vibrations of flexible manipulators (FM) are suppressed using active or passive control techniques. Suppressing vibrations increases the dynamic performance of the FM in engineering applications. In this study, a model extraction approach is proposed for vibration suppression of single-link flexible smart and composite manipulators. Active and passive control (APC) of residual vibrations is studied theoretically and experimentally. The smart manipulator consists of patching a piezoelectric (PZT) actuator to an aluminum and composite link. The finite element (FE) model of smart manipulators, including revolute joint and PZT actuator, is created in ANSYS. The motion profile and actuator voltage are the inputs, the tip displacement is the output. Then, the state-space (SS) mathematical models of the smart manipulators are extracted from the FE models by using the inputs and outputs. The open-loop and closed-loop simulations are performed using the extracted mathematical models in MATLAB. Passive control is achieved by the motion profiles, while active control is achieved by the PZT actuators. The PD controller with the displacement feedback is used to create the actuation voltages. For the optimized APC, the PD gains are optimized with a genetic algorithm by using the integral of the squared error and integral of absolute magnitude of the error fitness functions. Residual vibrations of smart manipulators are successfully reduced by the optimized APC. To verify the simulation results, open-loop and closed-loop experiments are carried out. The SS mathematical model successfully predicts the dynamic performance of FSM for various motion profiles, according to experimental results.
AtıfScopus12WoS9OpenAlex10İncele - MakaleAçık Erişim2021Scopus SJRQ1A comparative thermoeconomic analysis and optimization of two different combined cycles by utilizing waste heat source of an MSWPP
Ozahi, Emrah; Abusoglu, Aysegul; Tozlu, Alperen
In this paper, thermodynamic and thermoeconomic analysis as well as genetic algorithm optimization of two combined cycles, a gas turbine-organic Rankine cycle (GT-ORC) and a gas turbine-Kalina cycle (GT-KAL) are carried out. The novelty of this study is that the cycles are adapted to an actual solid waste power plant to generate additional power from the exhaust gas. Thus, the power generation capacity of the actual power plant can be raised by using the combined cycle. Due to this reason, besides the thermodynamic analysis of the cycles, thermoeconomic analyses and optimizations are also very important in order to improve the actual system capacity. The net power output of GT-ORC and GT-KAL are found to be 1.51 MW and 1.59 MW, respectively. The results obtained are seen to be reasonable when compared to the net power output of the power plant (5.66 MW). Another originality of this study is that the thermoeconomic results are improved by utilizing a multi-objective optimization method namely non-dominated sorting genetic algorithm method (NSGA-II). Thus, the two objectives, total power output and the total cost rate, at the design stage of the cycles are optimized and enhanced. Due to the optimization results, it is found that the net power output of the GT-ORC and GT-KAL are increased by 11.34% and 0.99%, respectively, while the total cost rates are decreased by 18.59% and 1.31%, respectively. GT-ORC with the net power output of 1.70 MW is seen to be more efficient as compared to GT-KAL which produces a net power output of 1.61 MW. However, the total and the capital cost rates of GT-ORC are found to be higher than those of GT-KAL.
AtıfScopus7WoS5OpenAlex11İncele - MakaleYalnız künye2020Scopus SJRQ1Optimization of an adapted Kalina cycle to an actual municipal solid waste power plant by using NSGA-II method
Özahi, Emrah; Tozlu, Alperen
In this paper thermodynamic and thermoeconomic analyses and even optimization of a Kalina cycle (KC) which is adapted to an actual solid waste power plant with a 5.66 MW installed capacity are presented as an alternative solution which is utilized to produce additional power from the exhaust gas of the plant. Up to now there is almost no study related with an adapted KC to a typical municipal solid waste power plant, and also no study based on an optimization, thermodynamic and thermoeconomic analyses of such a system together. All these facts show the novelty of this study. Herein the waste heat with a temperature of 566 °C is utilized by the adapted KC. According to the analyses of the first and second law of thermodynamics on the system, it is deduced that the electricity of 954.6 kW can be produced with the exergy efficiency of 24.15%. Furthermore, this power production can be improved by using non-dominated sorting genetic algorithm method (NSGA-II) in MATLAB software program. According to the optimization study, the deviations of the net power output and the total cost rate are found to be +3.62% and −1.47 $/h, respectively for the cycle.
AtıfScopus45WoS36OpenAlex49İncele - MakaleAçık Erişim2018Scopus SJRQ1Thermoeconomic multi-objective optimization of an organic Rankine cycle (ORC) adapted to an existing solid waste power plant
Özahi, Emrah; Tozlu, Alperen; Abuşoğlu, Ayşegül
In this paper, thermodynamic and thermoeconomic analyses, and also optimization of an organic Rankine cycle (ORC) were performed. The system was adapted to an existing solid waste power plant with a 5.66 MW installed power capacity in order to produce additional power from the exhaust gas. The actual operating data of the plant were utilized during all stages of the analyses. The originality of this paper is based on the analysis of the possibility of the energy conversion of an exhaust gas with a temperature of 566 & #x000B0;C into the electricity by utilizing an ORC system in the concept of waste-to-energy. Four different working fluids: toluene, octamethyltrisiloxane (MDM), octamethyl cyclotetrasiloxane (D4) and n-decane were considered and analyzed for the current system. This is also another novelty of this study due to lack of such a study, in the open literature, that deals with an ORC utilized for a typical municipal solid waste power plant. According to the thermoeconomic analyses, toluene was found to be the optimum working fluid with the maximum power output of 584.6 kW and the exergy efficiency of 15.69%. The optimization of the cycle was performed by using the non-dominated sorting genetic algorithm method (NSGA-II) in MATLAB software environment. The optimization results were compared and the deviations of the net power output and the total cost rate were evaluated as & #x2212;5.89%, & #x2212;3.51 & #x00024;/h for toluene; 0.96%, & #x2212;3.60 & #x00024;/h for MDM; 8.45%, & #x2212;2.04 & #x00024;/h for D4 and 2.00%, & #x2212;5.54 & #x00024;/h for n-decane, respectively.
AtıfScopus87WoS80OpenAlex88İncele - MakaleYalnız künye2018Scopus SJRQ2Optimum weight design of steel space frames with semi-rigid connections using harmony search and genetic algorithms
Artar, Musa; Daloğlu, Ayşe T.
In this paper, an optimization process using MATLAB-SAP2000 Open Application Programming Interface (OAPI) is presented for optimum design of space frames with semi-rigid connections. A specified list including W-profiles taken from American Institute of Steel Construction (AISC) is used in the selection of suitable sections. The stress constraints as indicated in load and resistance factor design of AISC, lateral displacement constraints being the top- and inter-storey drift and geometric constraints are considered in the optimization process. Genetic algorithm method based on biological principles and harmony search algorithm method based on the processes of musical harmony are used for optimum designs. Two different space frames are solved for the cases of rigid and semi-rigid connections, separately. A computer program is coded in MATLAB for the purpose interacting with SAP2000 OAPI. Results obtained from the analyses show that type of semi-rigid connections plays a crucial role in the optimization of steel space frames and increases the optimum weight.
AtıfScopus23WoS17OpenAlex30İncele - MakaleYalnız künye2018Scopus SJRQ1Thermoeconomic analysis and optimization of a Re-compression supercritical CO2 cycle using waste heat of Gaziantep Municipal Solid Waste Power Plant
Tozlu, Alperen; Abuşoğlu, Ayşegül; Özahi, Emrah
This paper presents thermodynamic and thermoeconomic analyses as well as optimization of a re-compression supercritical CO2 cycle. A gas turbine cycle (GT) is adapted as a model to an existing plant to generate additional power in Gaziantep Municipal Solid Waste Power Plant (GMSWPP). The total capital cost rate and total cost rate of the GT cycle are found to be 20.47 $/h and 77.14 $/h, respectively utilizing SPECO by using the exhaust gas of 16 kg/s with 1.9 bar and 566.7 °c. The net power, the energy and exergy efficiencies, the total cost and the total capital cost rates of the GT cycle are optimized by +1.73%, +3.21%, +2.45%, −1.11% and −1.64%, respectively using NSGA-II in MATLAB in the range of 2.5≤PR≤4, 200≤P6≤216, 16≤T0≤23 and 9.1≤LMTD≤12.9. This paper provides an originality such that optimization as well as thermodynamic and thermoeconomic analyses is performed simultaneously for an existing MSW power plant, which can be stressed that there are scarce amounts of studies related on this field. Moreover, as another novelty, it can be emphasized that net power output of such like plants which have similar capacity can be improved using the developed model and NSGA-II optimization method.
AtıfScopus47WoS43OpenAlex48İncele - MakaleAçık Erişim2017Scopus SJRQ2Estimation of physical properties of laminated composites via the method of inverse vibration problem
Balci, Murat; Gundogdu, Omer
In this study, estimation of some physical properties of a laminated composite plate was conducted via the inverse vibration problem. Laminated composite plate was modelled and simulated to obtain vibration responses for different length-to-thickness ratio in ANSYS. Furthermore, a numerical finite element model was developed for the laminated composite utilizing the Kirchhoff plate theory and programmed in MATLAB for simulations. Optimizing the difference between these two vibration responses, inverse vibration problem was solved to obtain some of the physical properties of the laminated composite using genetic algorithms. The estimated parameters are compared with the theoretical results, and a very good correspondence was observed.
AtıfScopus4WoS4OpenAlex7İncele - MakaleAçık Erişim2016Scopus SJRQ1A comparative study on optimum design of multi-element truss structures
Artar, Musa
A Harmony Search (HS) and Genetic Algorithms (GA), two powerful metaheuristic search techniques, are used for minimum weight designs of different truss structures by selecting suitable profile sections from a specified list taken from American Institute of Steel Construction (AISC). A computer program is coded in MATLAB interacting with SAP2000-OAPI to obtain solution of design problems. The stress constraints according to AISC-ASD (Allowable Stress Design) and displacement constraints are considered for optimum designs. Three different truss structures such as bridge, dome and tower structures taken from literature are designed and the results are compared with the ones available in literature. The results obtained from the solutions for truss structures show that optimum designs by these techniques are very similar to the literature results and HS method usually provides more economical solutions in multi-element truss problems.
AtıfScopus30WoS26İncele - MakaleYalnız künye2015DAMAGE DETECTION IN SIMULATED SPACE FRAMES USING GENETIC ALGORITHMS
Artar, Musa; Daloglu, Ayse T.
Genetic algorithms (GA) based finite element model updating are applied to predict damage location and severity in space frames. The changes in natural frequencies are used as dynamic indicators to describe damaged members. Objective functions including dynamic data provide minimization of dynamic differences between numerical model and simulated damaged model. The presence of damages in structural elements is identified by stiffness reduction as a reduction in modulus of elasticity. Reproduction, double-point crossover and mutation operators are used in GA optimization procedures. In this paper, different simulated examples having various damage scenarios are modelled in SAP2000 software to obtain the experimental dynamic data. In the last example, noise effect is taken into account in simulated damaged data. A program is developed in MATLAB software for numerical model updating based on all genetic algorithm procedures. Thus, the size and extent of simulated damages are determined by updated numerical model. Results obtained from examples show that GA optimization is a convenient method for damage identification.
AtıfWoS2İncele - MakaleYalnız künye2015Scopus SJRQ4The Optimization of Multi-Storey Composite Steel Frames with Genetic Algorithm Including Dynamic Constraints
Artar, Musa; Daloglu, Ayse
Optimum design of the structures, in other words designing the structures with minimum weight is one of the major research areas in structural engineering. The priority during optimization process is to ensure whether the necessary conditions are satisfied or not. In this study, the optimization of steel frame systems is carried out for traditional and dynamic constraints by using a genetic algorithm that mimics the biological processes. The stress constraints are determined according to TS648-Turkish code for design and construction of steel structures. Furthermore, displacement constrains are considered in the optimization procedure. In addition, natural frequencies are incorporated as dynamic constraints. Optimum design of multi-story plane frames is obtained and comparisons with the results of previous studies are made. The same design processes are repeated for the case of frames with composite beams. A program is coded in MATLAB to carry out all these applications. Results obtained in the study for the frame systems are also verified by SAP2000. It is concluded that the weight of the frames with composite beams are fewer and the dynamic constraints affect the design.
AtıfWoS6İncele - MakaleAçık Erişim2015Scopus SJRQ2Optimum design of steel space frames with composite beams using genetic algorithm
Artar, Musa; Daloʇlu, Ayşe T.
This paper presents an optimization process using Genetic Algorithm (GA) for minimum weight by selecting suitable standard sections from a specified list taken from American Institute of Steel Construction (AISC). The stress constraints obeying AISC-LRFD (American Institute of Steel Construction - Load and Resistance Factor Design), lateral displacement constraints being the top and inter-storey drift, mid-span deflection constraints for the beams and geometric constraints are considered for optimum design by using GA that mimics biological processes. Optimum designs for three different space frames taken from the literature are carried out first without considering concrete slab effects in finite element analyses for the constraints above and the results are compared with the ones available in literature. The same optimization procedures are then repeated for the case of space frames with composite (steel and concrete) beams. A program is coded in MATLAB for the optimization processes. Results obtained in the study showed that consideration of the contribution of the concrete on the behavior of the floor beams results with less steel weight and ends up with more economical designs.
AtıfScopus18İncele - MakaleYalnız künye2015Scopus SJRQ2Optimum design of steel frames with semi-rigid connections and composite beams
Artar, Musa; Daloʇlu, Ayşe T.
In this paper, an optimization process using Genetic Algorithm (GA) that mimics biological processes is presented for optimum design of planar frames with semi-rigid connections by selecting suitable standard sections from a specified list taken from American Institute of Steel Construction (AISC). The stress constraints as indicated in AISC-LRFD (American Institute of Steel Construction - Load and Resistance Factor Design), maximum lateral displacement constraints and geometric constraints are considered for optimum design. Two different planar frames with semi-rigid connections taken from the literature are carried out first without considering concrete slab effects in finite element analyses and the results are compared with the ones available in literature. The same optimization procedures are then repeated for full and semi rigid planar frames with composite (steel and concrete) beams. A program is developed in MATLAB for all optimization procedures. Results obtained from this study proved that consideration of the contribution of the concrete on the behavior of the floor beams provides lighter planar frames.
AtıfScopus16WoS16OpenAlex9İncele - MakaleAçık Erişim2015Scopus SJRQ2Optimum design of composite steel frames with semi-rigid connections and column bases via genetic algorithm
Artar, Musa; Daloʇlu, Ayşe T.
A genetic algorithm-based minimum weight design method is presented for steel frames containing composite beams, semi-rigid connections and column bases. Genetic Algorithms carry out optimum steel frames by selecting suitable profile sections from a specified list including 128 W sections taken from American Institute of Steel Construction (AISC). The displacement and stress constraints obeying AISC Allowable Stress Design (ASD) specification and geometric (size) constraints are incorporated in the optimization process. Optimum designs of three different plane frames with semi-rigid beam-to-column and column-to-base plate connections are carried out first without considering concrete slab effects on floor beams in finite element analyses. The same optimization procedures are then repeated for the case of frames with composite beams. A program is coded in MATLAB for all optimization procedures. Results obtained from the examples show the applicability and robustness of the method. Moreover, it is proved that consideration of the contribution of concrete on the behavior of the floor beams enables a lighter and more economical design for steel frames with semi-rigid connections and column bases.
AtıfScopus26WoS25OpenAlex21İncele - MakaleAçık Erişim2013DETERMINATION OF PHYSICAL PROPERTIES OF LAMINATED COMPOSITE BEAM VIA THE INVERSE VIBRATION PROBLEM METHOD
Balci, Murat; Gündogdu, Omer
In this study, some physical properties of a laminated composite beam were estimated by using the inverse vibration problem method. Laminated composite plate was modeled and simulated to obtain vibration responses for different length-to-thickness ratios in ANSYS. A numerical model of the laminated composite beam with unknown parameters was also developed using a two-dimensional finite element model by utilizing the Euler-Bernoulli beam theory. Then, these two models were embedded into the optimization program to form the objective function to be minimized using genetic algorithms. After minimizing the squared difference of the natural frequencies from these two models, the unknown parameters of the laminated composite beam were found. It is observed in this study that the Euler-Bernoulli beam theory suppositions approximated the real results with a rate of %0.026 error as the thickness of the beam got thinner. The estimated values were finally compared with the expected values and a very good correspondence was observed.
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