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The city of Baghdad is currently facing a significant air pollution crisis due to increased industrial activity. Therefore, the assessment of concentrations of air pollutants specifically carbon dioxide (CO2), sulfur dioxide (SO2), nitrogen dioxide (NO2), nitrous oxide (N2O), and methane (CH4) at Al-Mustansiriya University, located approximately 10 km from the north of the South Baghdad Thermal Power Plant (SBTPP), has been made and the emission rates of these pollutants are estimated.  The atmospheric stability was determined using a three-dimensional ultrasonic anemometer and stability classes were determined using the Monin-Obukhov method and applied Lagrange scale to calculate vertical and horizontal dispersion coefficients. We applied the Gaussian model to the dataset in July 2024, a month characterized by peak power generation and increased fuel combustion. The results showed that the vertical dispersion coefficient played an important role more than the transverse dispersion coefficient in measuring the dispersion of pollutants causing instability in the atmosphere. A significant peak around the tenth day of the month was observed, suggesting a change in winds, temperature, or weather patterns that influenced the dispersion and accumulation of these gases. The concentrations of the gases were found to vary with distance. The analysis indicated that the pollutants from the plant primarily dispersed in a north-westerly direction due to prevalent wind direction and their impact on areas near the Al-Mustansiriya University.

The main stages in choosing the optimal technology for thermal treatment of MSW (municipal solid wastes) at the stage of feasibility study are discussed. The analysis of the influence of the fuel component on the structure of the WTE plant is carried out. Recommendations have been developed for choosing the optimal energy structure of a WTE plant, ash and slag management schemes, and thermal utilization technologies that are most suitable for the location of the facility. A study was conducted on the influence of the selected structure of the energy complex on the decision to choose a gas treatment system. A comparison of criteria for choosing the structure of the energy complex at the MSW in comparison with the energy thermal power plant (TPP) is made. Due to the economic and environmental problems that appear during the construction of WTE plants, when it comes to waste scheme for cities and regions, it is recommended to use a scheme with the maximum use of secondary raw materials from waste and thermal treatment only of residues that are not subject to recycling, from which the fractions containing the largest amount of pollutants (RDF fuel) are extracted. The technology using deep recovery of secondary raw materials will simplify the structure of WTE plant, reduce capital and operating costs, and most importantly reduce emissions of harmful substances into the atmosphere with flue gases, ash and slag.

Polycyclic aromatic hydrocarbons (PAHs) were measured in soil samples, which collected for the period from January to August 2017, in Baghdad city, at AL-zubaidiya Thermal Power Plant. Soil samples were extracted by the soxhlet apparatus using mixture of acetone and hexane (1:1), and analyzed by GC/FID apparatus.The results show that the sixteen polycyclic aromatic hydrocarbon concentrations were varied in the two seasons. The most abundant compound in winter was naphthalene, while in summer season were naphthalene, acenaphthene, fluorene, pyrene and benzo[a]anthracene. The distribution of PAHs that have different aromatics ring, where the 2-rings are dominant in the winter season, while 2-rings, 3-rings, 4-rings are exists in summer season.

The greenhouse gas (GHG) emissions inventories in our context result from the production of electricity from fuel oil at the Mbalmayo thermal power plant between 2016 and 2020. Our study area is located in the Central Cameroon region. The empirical method of the second level of industrialisation was applied to estimate GHG emissions and the application of the genetic algorithm-Gaussian (GA-Gaussian) coupling method was used to optimise the estimation of GHG emissions. Our work is of an experimental nature and aims to estimate the quantities of GHG produced by the Mbalmayo thermal power plant during its operation. The search for the best objective function using genetic algorithms is designed to bring us closer to the best concentration, and the Gaussian model is used to estimate the concentration level. The results obtained show that the average monthly emissions in kilograms (kg) of GHGs from the Mbalmayo thermal power plant are: 526 kg for carbon dioxide (CO 2 ), 971.41 kg for methane (CH 4 ) and 309.41 kg for nitrous oxide (N 2 O), for an average monthly production of 6058.12 kWh of energy. Evaluation of the stack height shows that increasing the stack height helps to reduce local GHG concentrations. According to the Cameroonian standards published in 2021, the limit concentrations of GHGs remain below 30 mg/m 3 for CO 2 and 200 μg/m 3 for N 2 O, while for CH 4 we reach the limit value of 60 μg/m 3 . These results will enable the authorities to take appropriate measures to reduce GHG concentrations.

Increasing the energy efficiency of thermal power plants operating according to the Rankine cycle is one of the priority tasks of the Russian energy sector. Despite a significant amount of scientific research, the efficiency of installations of this type still remains low. As a technological solution to increase their efficiency, the authors consider a modernized Rankine cycle in which an aqueous solution of lithium bromide is used as a working fluid, the condensation process of exhaust steam after the turbine is replaced by the process of its absorption, and the second working fluid is an absorbent. The features of the functioning of such a cycle are outlined, and the methodology for its calculation is presented. Studies have shown that the use of lithium bromide solution can reduce the steam pressure after the turbine and increase the useful heat drop as well as the degree of cycle filling. In addition, when the heat of the solution returned from the boiler is regenerated, the average temperature of the heat supply to the cycle increases, which also increases its thermal efficiency compared to the traditional circuit. The energy efficiency of the modernized cycle was analyzed and compared with the traditional Rankine cycle on water vapor. Calculations have shown that the use of a modernized cycle allows increasing thermal efficiency by an average of 1–2% compared to the traditional solution. The indicators characteristic of both steam power and absorption cycles were studied, and graphical dependences of efficiency on the main parameters were derived. The economic effect of using the modernized scheme is to reduce fuel consumption and emissions of harmful substances into the atmosphere in proportion to the reduction in fuel consumption.

The impacts of climate change, combined with the depletion of fossil fuel reserves, are forcing human civilizations to reconsider the design of electricity generation systems to gradually and extensively incorporate renewable energies. This study aims to investigate the technical and economic aspects of replacing all heavy fuel oil (HFO) and light fuel oil (LFO) thermal power plants connected to the electricity grid in southern Cameroon. The proposed renewable energy system consists of a solar photovoltaic (PV) field, a pumped hydroelectric energy storage (PHES) system, and an ultra-capacitor energy storage system. The economic and technical performance of the new renewable energy system was assessed using metrics such as total annualized project cost (TAC), loss of load probability (LOLP), and loss of power supply probability (LPSP). The Multi-Objective Bonobo Optimizer (MOBO) was used to both size the components of the new renewable energy system and choose the best location for the solar PV array. The results achieved using MOBO were superior to those obtained from other known optimization techniques. Using metaheuristics for renewable energy system sizing necessitated the creation of mathematical models of renewable energy system components and techno-economic decision criteria under MATLAB software. Based on the results for the deficit rate (LPSP) of zero, the installation of the photovoltaic field in Bafoussam had the lowest TAC of around 52.78 × 10 6 € when compared to the results for Yaoundé, Bamenda, Douala, and Limbe. Finally, the project profitability analysis determined that the project is financially viable when the energy produced by the renewable energy systems is sold at an average price of 0.12 €/kWh.

In this paper, a universal method has been developed to determine the price of a hydro resource (one cubic meter) for the operational regulation of a hydropower plant (HPP), which is a combination of an optimization method and a method for assessing the marginal utility. The proposed approach is based on the correct representation of differential incremental rate characteristics of water at an HPP and fuel at a thermal power plant (TPP). To know the price of a hydro resource used for electricity generation at a hydropower plant. This gives the possibility to increase the efficiency of management both at a hydropower plant, and in a water utilization system as a whole. Using the examples of Novosibirsk HPP, it is expected to develop an estimation of economic effect from the implementation of the developed criteria, the proposed method of the calculation of a hydro resource price at HPP, and the method of separating fuel costs at CHPP. As a result of the implementation the developed method for the HPP, a price of electricity sold in the flexible energy market will be compared to the price of the electricity produced and sold at CHPP, being equal to approximately 330 rubles/MW h.

Steam condensers are vital components of thermal power plants, responsible for converting turbine exhaust steam back into water for reuse in the power generation cycle. Effective pressure regulation is crucial to ensure operational efficiency and equipment safety. However, conventional control strategies, such as PI, PI-PDn and FOPID controllers, often struggle to manage the nonlinearities and disturbances inherent in steam condenser systems. This paper introduces a novel multistage controller, TDn(1 + PIDn), optimized using the diligent crow search algorithm (DCSA). The proposed controller is specifically designed to address system nonlinearities, external disturbances, and the complexities of dynamic responses in steam condensers. Key contributions include the development of a flexible multi-stage control framework and its optimization via DCSA to achieve enhanced stability, faster response times, and reduced steady-state errors. Simulation results demonstrate that the TDn(1 + PIDn) controller outperforms conventional control strategies, including those tuned with advanced metaheuristic algorithms, in terms of settling time, overshoot, and integral of time weighted absolute error (ITAE). This study marks a significant advancement in pressure regulation strategies, providing a robust and adaptive solution for nonlinear industrial systems.

Wet cooling towers (WCT) are widely used to reject the unutilized heat in coal thermal power plants (TPPs). But this comes at the cost of excessive water consumption. Adoption of air-cooled condensers (ACC), also known as dry cooling systems, in all proposed Indian TPPs would reduce their water consumption by 26% in 2030. However, power producers are reluctant to install ACC due to technical and economic disadvantages such as high capital investment and land footprint. This study evaluates the major challenges in implementing ACC by quantifying them in terms of cost of electricity generation. Critical parameters of WCT and ACC, such as water and auxiliary consumption, are also estimated at varying ambient air conditions. The study shows that cost of electricity generation in TPPs with ACC would increase by 0.26–0.30 INR/kWh (0.37–0.42 US cents/kWh) compared to those TPPs with WCT. Despite this, installation of ACC would still be economically viable for those TPPs that are susceptible to at least 1 month of shutdown annually due to water shortages. On an average, Indian plants that are located at high water-stressed regions operate 1.5 months lesser than those at low and medium water-stressed regions. Such TPPs would see an increase in cost of electricity generation by 0.17 INR/kWh (0.24 US cents/kWh) compared to TPPs with ACC.

The global energy demand is still primarily reliant on fossil-fueled thermal power plants. With the growing share of renewables, these plants must frequently adjust their loads. Maintaining, or ideally increasing operational efficiency under these conditions is crucial. Increasing the efficiency of such systems directly reduces associated greenhouse gas emissions, but it requires sophisticated models and monitoring systems. Data-driven models have proven their value here, as they can be used for monitoring, operational state estimation, and prediction. However, they are also sensitive to (1) the training approach, (2) the selected feature set, (3) and the algorithm used. Using operational data, we comprehensively investigate these model parameters for performance prediction in a thermal plant for process steam generation. Specifically, four regression algorithms are evaluated for the prediction of the highly fluctuating live steam flow with two training approaches and three feature subsets of the raw dataset. Furthermore, manual and automatic clustering methods are used to identify different states of operation regarding the fuel amounts used in the combustion chamber. Our results show that the live steam flow is predicted with excellent accuracy for a testing period of one month ( R 2 =0.994 and NMAE=0.55%) when using a dynamic training approach and a comprehensive feature set comprised of 48 features representing the combustion process. It is also seen that the statically trained model predicts various load changes with strong accuracy and that the accuracy of the dynamically trained model can be approached by incorporating the cluster information into the static model. These models reflect the plant’s physical intricacies under varying loads, where deviations from the predicted live steam flow indicate unwanted long-term drifts. They can be directly implemented to help operators detect inefficiencies and optimize plant performance.