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The prominence of managing municipal solid waste (MSW) in an efficient and effective manner is increasing from day to day. In this paper, the solid waste management (SWM) system of İstanbul is analyzed by applying the techniques from mathematical programming methodology. In this manner, the solutions of the two optimization problems which aim to minimize the total cost and the environmental effects of SWM, respectively, are presented in this study. Additionally, a sensitivity analysis is performed and a multi-objective problem that combines two problems is presented. In this regard, the application of five MSW management technologies which are currently in use in İstanbul on six waste components is analyzed; and the optimal solution regarding the best mixture of these technologies is developed on a given waste composition. Besides, this optimal solution is compared with the current practice in İstanbul; and recommendations are presented about possible future investments for the policymakers. The results of the study emphasize the importance of material recovery and incineration facilities to improve profitability and to minimize environmental side effects. In particular, material recovery facility (MRF) should be expanded to be able to treat all of metal, paper and plastic from a cost management perspective. Incineration (INC) facility should also be expanded in order to treat plastics or organic waste from a Greenhouse Gas (GHG) minimization perspective. In addition to this, landfill appears to be the most prominent treatment technique according to the current problem parameters. However, regarding the waste composition, the amount of organic waste must be decreased by more than 37% for other waste streams to be treated in different facilities other than landfill. Anaerobic digestion and composting facilities need to be more cost-effective for becoming economically feasible. The methodology represented in this study can be extended and generalized to other cities around the world once the correct problem parameters are specified. Image 105510 • Applying multi-objective optimization to solid waste management problem. • Importance of material recovery and incineration to improve profitability. • Landfill appears to be the most prominent treatment technique. • Must decrease the operating costs of composting and anaerobic digestion processes. • Applicable method to other cities after specifying the correct problem parameters. [ABSTRACT FROM AUTHOR]

[Display omitted] • The most profitable system is on-grid PV-Wind Turbine system with an NPC of $52.8 M. • For 0% and 25% sale constrained systems low penetration systems are optimal. • For 50% sale constrained and unconstrained systems high penetration systems are preferred. • Fire hawk Optimizer, Grey Wolf Optimizer and Particle Swarm Optimizer are used to size the system by power flow analysis. Fire Hawk Optimizer is found to be usable in optimal power flow analysis and system sizing. • Increase in penetrations leads to enlargement of the system in the region with high load demand because of optimizations. Today, with a human population of 7.5 billion, increasing power consumption, and widespread use of high-technology equipment, the need for energy in the world is increasing day by day. The damage to nature caused by non-renewable energy resources and the economic and political disadvantages they cause in countries that must import these resources have led the world to search for alternative energy sources. One of the most important benefits of renewable energy sources is that they can create hybrid energy systems with other energy sources. Hybrid energy systems are structures in that more than one energy generation unit works together to feed the electrical load. In this paper, a hybrid system will be designed by using Homer PRO, to supply the energy consumed in the residential areas of Karaburun and Urla districts of Izmir province in Turkey with hybrid renewable energy systems. Systems of different sizes are designed for 50%, 75%, and 100% penetration rates, and sales to the grid are constrained to 0%, 25%, 50%, and no constraint on sales. Sensitivity analyses have been performed for 10% and 20% more power required by the load. Techno-economic analyses were done for the systems and the optimal system has been identified. The power flow of the optimal system is inspected by defining the network in Python and Fire Hawk Optimizer (FHO), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO) are used for optimal sizing of the system. The optimal system has a Net Present Cost (NPC) of $52.3 M, 34 wind turbines, and 954 kW PV with conditions of 100% penetration and no restrictions on sales to the grid. FHO proposed 26 wind turbines and 780 kW PVs for Urla and 8 wind turbines, and 174 kW PVs for Karaburun. GWO proposed 25 wind turbines and 747 kW PVs for Urla, 9 wind turbines, and 165 PVs for Karaburun. PSO offered 24 wind turbines and 708 kW PVs for Urla, 10 wind turbines, and 246 kW PVs for Karaburun. When there is no sale to and 25% sales to the grid, it is seen that the optimal scenario is the system with a 50% penetration rate. In fully sale-constrained systems, NPCs are higher than the base system and they can never pay for themselves compared to the base system. It is concluded that smaller-sized systems are more suitable for systems with high constraints. The proposed FHO algorithm and GWO algorithm react to increasing penetration ratios by enlarging the system size in the regions with high load demand. PSO sizes the system like predictions by load demand. FHO reacts quicker to increasing penetrations than PSO and GWO. FHO algorithm is found to be usable to solve optimal power flow problems and optimal system sizing. [ABSTRACT FROM AUTHOR]

This study investigates the potential for renewable energy-based electricity generation using existing wave, wind, and solar energies in Türkiye. A significant part of Türkiye's energy needs is still met using fossil fuels. Considering the country's resources, renewable energy sources appear as an alternative source to meet these needs. The objective of this study is to find an effective, efficient, economical, environmentally friendly, and sustainable way to produce electricity to reach net-zero targets and transition towards low-carbon and carbon-free energy systems. To be able to make a deep investigation about the relevant issue, six provinces from different regions of Türkiye (Antalya, Çanakkale, İstanbul, İzmir, Kırklareli, and Muğla) are assessed in terms of wave, wind, and solar energy potential, including wave data, wind speeds, sunshine duration, and global radiation values. Wind, wave, and solar energy data of the selected regions were taken from the ERA5 database, which is the weather forecast model of the European Center for Medium-Term Weather Forecasts (ECMWF), and the Ministry of Energy and Natural Resources of the Republic of Türkiye and the General Directorate of Meteorology. Calculations were made using monthly data for the last 5 years. Considering the coastal lengths in the determined regions, the annual total electrical power produced from wave, solar, and wind energies was calculated. In these calculations, the coastal length parameter was assumed to be uniform across all cities, and the electrical power potential from these energy sources was analyzed. Within the framework of these analyses, the number of houses in the selected regions whose electricity needs can be met was calculated. As a result, the potential electrical power and the amount of affordable housing units in the selected regions were compared. As an important result of the studies, it was determined that the characteristic features of the selected regions, such as wavelength, wave height, and wind speed, were directly related to the applicable coast length. The power obtained from wave energy was higher than that from other renewable energy sources, considering the determined coast lengths. Wave energy was followed by parabolic solar collector, wind, and photovoltaic solar energy systems. According to the model, the power obtained from renewable energy systems was at the highest level in the Kırklareli/Demirköy province compared to other locations. Kırklareli was followed by İstanbul, Antalya, İzmir, Muğla, and Çanakkale. It was also found that the electricity needs of 763,578 houses were met in the Kırklareli/Demirköy region, and the electricity needs of 470,590 houses were met in the Çanakkale/Ayvacık region. The statistically optimized factors using the Response Surface Methodology (RSM) for wind, photovoltaic, parabolic solar collector, and wave power were reported as 995.278, 4529.743, 2264.546, and 276,495.09, respectively. The optimal factors aim to achieve a total electricity generation rate of 2.491 × 109 (kWh/year), a total number of houses of 682,590.55 (number/year), and a total cost of USD 813,940,876. In line with the results obtained, the Kırklareli/Demirköy region becomes favorable when considering wave and wave-integrated wind and solar energies. The proposed system has the potential to meet the entire electricity demand of the Kırklareli province based on data from the Republic of Türkiye Energy Market Regulatory Authority (EMRA). [ABSTRACT FROM AUTHOR]

This paper deals with a sustainable personnel scheduling problem of personnel working in a large-scale natural gas combined-cycle power plant in Turkey. The proposed model focuses on employee complaints due to unfair work schedules and the results of balanced assignments based on power plant interruptions. Eighty personnel work in three shifts at this natural gas combined-cycle power plant. The model is solved with respect to some of the workers' skills, and there are 20 criteria regarding skills. The analytic network process method is used to get the weights of workers' skills, which are calculated and included in the model. Goal programming is used in this paper. Our proposed model gives cost minimization and fair work schedules for the power plant. Compared with the literature, the number and set of criteria are unique in terms of personnel competency in the energy sector. Minimizing cost and imbalanced assignments was achieved by the proposed model for the first time without considering the sector. [ABSTRACT FROM AUTHOR]

Land consolidation (LC) is a widely used spatial land management tool. The most essential stage of the LC process is land reallocation, which comprises the exchange of property rights. For this reason, land reallocation may be a potential source of dissatisfaction among farmers, and thus, optimization techniques are continually being developed to minimize these dissatisfactions. The goal of this study was to develop a novel preference‐based artificial‐bee‐colony (ABC) algorithm for use in the land reallocation project for DOT village. In the study, the farmer preferences were prioritized. Specifically, 70% of the lands were preference‐based, with the remainder being random. Furthermore, the ABC algorithm's performance was assessed using three limit parameter (LP) values and two probability functions. The best success was attained when the LP was set to 50 and the roulette‐wheel probability function was employed. The success of land reallocation was defined by the occupancy rate in the blocks and the reallocation rates based on the preferences of the farmer's lands. The occupancy rate in all the blocks using the ABC algorithm was 95.54%, the full occupancy rate of blocks according to farmer preferences was 38%, and the land reallocation rate based on the farmers' preferences was 73%. The remaining lands were distributed to the block areas at random using the ABC method. Moreover, the success of the preference‐based population surpassed that of randomly formed populations. [ABSTRACT FROM AUTHOR]

In this research, the advantages of the e-scooter tool used in the mail or package delivery process were discussed by considering the Turkish Post Office (PTT) data in the districts of Istanbul (Kadıköy, Üsküdar, Kartal, and Maltepe) in Turkey. The optimization Poisson regression model was utilized to deliver the maximum number of packages or mails with minimum cost and the shortest time in terms of energy consumption, cost, and environmental contribution. Statistical and optimization results of dependent and independent variables were calculated using numerical and categorical features of 100 e-scooter drivers. The Poisson regression analysis determined that the e-scooter driver's gender (p|0.05 < 0.199) and age (p|0.05 < 0.679) factors were not effective on the dependent variable. We analysed that the experience in the profession (tenure), the size of the area responsible, and environmental factors is effective in the e-scooter distribution activity. The number of packages delivered was 234 in a day, and the delivery cost per package was calculated as 0.51 TL (Turkish Lira) for the optimum values of the dependent variables. The findings show that the choice of e-scooter vehicle in the mail or package delivery process is beneficial in terms of time, cost, energy, and environmental contribution in districts with higher population density. As the most important result, the operation of e-scooter vehicles with electrical energy shows that it is environmentally friendly and has no CO2 emission. The fact that the distribution of packages or mail should now turn to micro-mobility is emerging with the advantages of e-scooter vehicles in the mail and package delivery. Finally, this analysis aims to provide a model for integrating e-scooters in package or mail delivery to local authorities, especially in densely populated areas. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, size of a PV/wind integrated hybrid energy system with battery storage is optimized under various loads and unit cost of auxiliary energy sources. The optimization is completed by a simulation based optimization procedure, OptQuest, which integrates various heuristic methods. In the study, the main performance measure is the hybrid energy system cost. And the design parameters are PV size, wind turbine rotor swept area and the battery capacity. The case study is realized for Izmir Institute of Technology Campus Area, Urla, Turkey. The simulation model of the system is realized in ARENA 12.0, a commercial simulation software, and is optimized using the OptQuest tool in this software. Consequently, the optimum sizes of PV, wind turbine and battery capacity are obtained under various auxiliary energy unit costs and two different loads. The optimum results are confirmed using Loss of Load Probability (LLP) and autonomy analysis. And the investment costs are investigated how they are shared among those four energy sources at the optimum points. [Copyright &y& Elsevier]

Today, the building sector has a large share in global energy consumption. The building stock, which has increased with the increasing population, contributes more to this consumption day by day. School buildings, which constitute an important part of public buildings, are among the most used building types in the daily cycle and also have a high energy demand for heating, cooling and electricity loads. In Turkey, the implementation of the same type of school projects in different climate regions without considering the context and climatic conditions increases the amount of energy consumption. This study provides a framework for reducing energy loads in school buildings using passive design strategies. Located in two different climatic regions of Turkey, in Istanbul and Ankara, a primary school building is taken into consideration for comparison of type projects in the context of energy performance and single-objective optimization process to minimize energy use intensity (EUI). As a result of the optimization processes carried out depending on the parameters related to the building envelope, the energy performance for the Istanbul climate is improved by 5.05%, while this rate is 4.09% for the Ankara climate. The best solution individual parameters of both climate types have very close values to each other. It is thought that more efficient results can be obtained with the multi-objective optimization processes of the studies to be carried out to evaluate the type school projects on the basis of different climates. [ABSTRACT FROM AUTHOR]

Microalgae known as third generation technology for biofuel in nature are used as a renewable bioenergy source. Microalgae and crop plants have common for the production of organic compounds by using sunlight and carbon dioxide. In addition, microalgae are capable of being reproducing in the whole year allowing the more product yield than those of plants. Therefore, microalgae are more favorable feedstock since they have some advantages such as photosynthetic efficiency, biomass productivity and oil content. In this study, the most proper conditions for the growth of microalgae Chlorella vulgaris were studied. After medium optimization, at 25°C and pH of 9 using 24-hour-illuminating/day, the best growth conditions for C. vulgaris giving the maximum biomass productivity was found to be 205 mg/L for 10 days. Determination of optimal microalgal growth conditions may lead to an increase in the industrial applications of C. vulgaris. [ABSTRACT FROM AUTHOR]

Solid waste is one of the important causes of the environmental crisis that negatively impacts human health throughout the world and is fast approaching a disaster level that will pose a direct threat to human life. As with all other environmental problems, the increase in solid waste production that goes hand in hand with growing population and rising consumption has become a focus of great concern. Along with these rising levels, the investment, management and maintenance of solid waste collection and transport vehicles is seeing a continual increase in financial outlay. It is clear from the budgets of local authority solid waste management systems, 65 to 80% of which are accounted for by domestic waste, that the collection and transport of solid waste is a high-cost process and that this expenditure can be significantly reduced by the reorganisation of solid waste collection routing schedules and the minimization of collection frequency. This study demonstrates a linear programming model in order to develop an optimal routing schedule for solid waste collection and transportation, thereby reducing costs to a minimum. The neighbourhood of Veysel Karani in the Haliliye District of Şanlıurfa Province, Turkey, was specifically selected for this case study, having the suitable socio-economic and demographic variables to be representative of a metropolitan urban area. Firstly, the data regarding the municipal solid waste collection and transport routes were obtained from the local authority. Analysis and verification of these data were then performed. With the field study, these data were verified on-site, and the missing data were completed. Linear programming and geographic information system (GIS) analysis were used to determine the best route. Consequently, it is concluded that it is possible to save the route by 28% with GIS analysis and 33% with linear programming analysis according to the existing municipal solid waste collection and transportation routes. [ABSTRACT FROM AUTHOR]

Determining appropriate management strategies to reduce greenhouse gas emissions using optimization techniques to understand how forest management activities affect the carbon dynamics is critical in implementing effective carbon management policies. This paper quantitatively analyzes the long-term effects of different management policies and silvicultural interventions using linear programming. In the analyses, afforestation targets for bare forest lands, tree species, carbon prices, planning approaches and sets of various targets and constraints on carbon dynamics were evaluated. The results were based on twenty-five forest management scenarios formulated for the Korucu Forest Planning Unit of Turkey. The results showed that, compared to timber-based planning strategies (TM), ecosystem-based planning approach (EM) contributes to a significant reduction in carbon sequestration in many cases. When different afforestation targets were incorporated into forest management strategies, cumulative carbon sequestration increased constantly compared to baseline scenario without any afforestation areas. In addition, the highest total carbon sequestration was observed when black pine (P. nigra) was used in afforestation activities rather than oak species (Quercus sp.) and other available tree species. While total timber production and timber net present value (NPV) decreased, carbon sequestration increased significantly with increasing carbon price. As a result of increasing carbon price from $20/ton to $100/ton, joint NPV increased by about five times. The results highlighted the importance of forest ecosystem and developing and implementing climate adaption measures into forest management activities in tackling climate change phenomenon. • Determining suitable management scenarios is critical for effective carbon policies. • Ecosystem management generally causes a significant reduction in carbon value. • Carbon economy is highly critical for the timber and carbon production. • Afforestation in suitable areas provides significant contribution to carbon stocks. • Afforestation with P.nigra is more effective for C stock than with Quercus sp. [ABSTRACT FROM AUTHOR]

Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS. [ABSTRACT FROM AUTHOR]

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A mixed integer goal programming model is developed to address the regeneration planning problems of even-aged forests in the Mediterranean region of Turkey. The unique aspect of the goal programming formulation is to minimize deviations in scheduled wood product volumes and the size of harvest areas within each time period, as these are important goals for the management area. About 98% of the forests in Turkey are considered even-aged, and 2% are uneven-aged. Therefore, an age class method is used for the planning of even-aged forests. For the areas where this method is applied, reaching the optimal age class structure is the first priority. This involves implementing final harvests (clearcuts) to regenerate an amount of forest area into each age class. To meet the local market's needs, forest enterprises also require the final yield to be fairly equal each year. Further, it is desired that the harvest area (regeneration area) is relatively equal each year, to address operational considerations. A linear goal programming model is developed to address the problem. The minimization of deviations from both the harvest area and harvest volume targets are incorporated as goals in the objective function of the model. Several scenarios are solved using the extended version of Lingo 16. A scenario with weights of 0.8 for area and 0.2 for volume produces the best results. Here, the total deviation for 20 years is 3.8 ha in area and 2889 m3 in volume. In the actual regeneration plan, the area deviation for 10 years is 54.72 ha (6.2% of total regeneration area), and the volume deviation is 20,472 m3 (9.8% of harvest volume). The model described through this study can be developed further and integrated into forest management planning software and processes used for the planning of even-aged forests in the Mediterranean region. [ABSTRACT FROM AUTHOR]