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Air pollution is widespread in the world and is considered one of the most important risk factors in Iraq, especially as a result of the lack of a green belt surrounding cities and the many causes of pollution, including traffic congestion, the spread of gas power plants and other causes of pollution. The most common factors of pollution in the air are the spread of gases that are harmful to human health, including monoxide, carbon dioxide (CO2), ozone and the spread of dust, which directly affects human health. A smart system has been proposed to measure levels of pollutants, of which carbon monoxide (CO), dioxide and dust are at the forefront. Several cities, including Baghdad, Karbala, Najaf and Hilla, were chosen to measure the percentage of disparity between pollutants in these cities, determine the percentage of CO2 on Google maps for these cities and update the data instantly by sending the data via the cloud computing. The implemented system consists of an Arduino Uno, a (MG811) sensor to measure CO2, a (MQ‐2) sensor to CO and a (DSM501A PM2.5) sensor to measure air quality and the percentage of dust in the atmosphere. The data was also sent via the (Time4vps) cloud computing so that the data were updated instantly. The results obtained showed a difference in the percentage of pollutants between cities and different periods during one day and in one city. The proposed system is very successful to ministry of health if it is implemented in all cities and all the regions of cities around the country because it gives the alert to make all health organizations ready to receipt the higher number of patients in the emergency cases. [ABSTRACT FROM AUTHOR]

Multi-criteria decision analysis (MCDA) methods are vital in assessing decision variants under multiple conditions. However, involving domain experts in developing decision models can be challenging and costly, necessitating more scalable and independent solutions. This paper introduces the intelligent characteristic objects method (INCOME), which combines the k-Nearest Neighbor (kNN) algorithm and the COMET method to create a theoretical decision-maker for comparing characteristic objects (COs). INCOME overcomes limitations of classical MCDA methods, such as the TOPSIS approach, which struggles with complex functions and non-monotonic modeling. INCOME influences data-based knowledge to provide a robust framework for assessing decision options. The integration of the COMET method and kNN algorithm enables improved modeling of decision functions based on evaluated data, increasing the flexibility and independence of the INCOME approach. A case study assessing gas power plants based on four criteria is presented to validate the performance of the INCOME method. The results demonstrate high correlations with the reference model and slightly higher classical approaches like TOPSIS and TOPSIS-COMET. However, INCOME exhibits greater stability and flexibility by utilizing all available data instead of relying on limited expert knowledge. The proposed INCOME approach offers several advantages, including creating a continuous decision model, resistance to the Rank-Reversal phenomenon, and the potential for replacing domain experts with artificial experts. This study highlights the effectiveness of INCOME in Multi-Criteria Decision Analysis. It suggests future research directions, such as parameter selection and testing in different decision-making problems. [ABSTRACT FROM AUTHOR]

Oxy-fuel combustion technology replaces air with a mixture of pure O2 and recycled flue gas for coal combustion, which leads to difficulties in the waste heat recovery of flue gas in the boiler tail of coal-fired power plants. This paper proposes a new integration scheme for waste heat recovery of flue gas in coal-fired power plants with oxy-fuel combustion CO2 capture. By introducing an oxygen preheater, a recycled flue gas preheater, and a low-pressure economizer, the waste heat of flue gas is fully recovered to preheat oxygen, recycled flue gas, and feed water, respectively. The proposed scheme simultaneously ensures the safe operation of the recycled fan and improves the thermal performance of the coal-fired power plants. Compared to the air combustion configuration, the boiler's efficiency and gross power efficiency in the oxy-fuel combustion configuration are increased by 0.42% and 1.29%, respectively. Due to power consumption for the added equipment, the net power efficiency is reduced by 10.41%. A techno-economic analysis shows that the cost of electricity for oxy-fuel combustion in coal-fired power plants has increased from USD 46.45/MWh to USD 80.18/MWh, and the cost of the CO2 avoided reaches USD 43.24/t CO2. [ABSTRACT FROM AUTHOR]

An ohmic heating device was fabricated. For the heating process in three input voltage gradients and three percent weight loss of the sample were selected. During the thermal process, the power consumption was calculated and then using the ratios of pollution in power plants, the amount of NOx, SO2, and CO2 in three Steam, Gas turbine and Combine cycle power plants was calculated. All experiments were performed in three replications using completely random design (CRD) statistical design and the results were analyzed using SAS software. The highest amount of NOx was first in the Gas turbine plant and then in the Combine cycle power plant and Steam power plant, respectively. The amount of SO2 in Heavy gas used in the Steam power plant is more than the other two power plants, and then the highest amount of SO2 is obtained in Gas turbine power plant and finally in Combine cycle power plant. Also, the amount of CO2 in the Combine cycle power plant was less than the other two types of power plants. In general, when the factors of ohmic voltage and weight loss percentage are considered for the ohmic process, natural gas and Combine cycle power plant create the best possible state. [ABSTRACT FROM AUTHOR]

In gas‐fired power plants, emissions may reduce turbine blade rotation, thus decreasing power output. This study proposes a hybrid model integrating the Feed forward Neural Network (FFNN) model and Particle Swarm Optimization (PSO) algorithm to predict gas emissions from natural gas power plants. The FFNN predicts gas turbine nitrogen oxides (NOx) and carbon monoxide (CO) emissions, while the PSO optimizes FFNN weights, improving prediction accuracy. The PSO adopts a unique random number selection strategy, incorporating the K‐Nearest Neighbor (KNN) algorithm to reduce prediction errors. Neighbor Component Analysis (NCA) selects parameters most correlated with CO and NOx emissions. The hybrid model is constructed, trained, and testedusing publicly available datasets, evaluating performance with statistical metrics like Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Results show significant improvement in FFNN training with the PSO algorithm, boosting CO and NOx prediction accuracy by 99.18% and 82.11%, respectively. The model achieves the lowest MSE, MAE, and RMSE values for CO and NOx emissions. Overall, the hybrid model achieves high prediction accuracy, particularly with optimized PSO parameter selection using seed random generators. [ABSTRACT FROM AUTHOR]

Since retrofitting existing natural gas-fired (NGF) power plants is an essential strategy for enhancing their efficiency and controlling greenhouse gas emissions, this paper compares the carbon footprint of natural gas-fired power generation from an NGF power plant in Brazil (BR-NGF) with and without retrofitting. The former scenario entails retrofitting the BR-NGF power plant with combined-cycle gas turbine (CCGT) technology. In contrast, the latter involves continuing the BR-NGF power plant operation with open-cycle gas turbine (OCGT) technology. Our analysis considers the BR-NGF power plant's life cycle (construction, operation, and decommissioning) and the natural gas' life cycle (natural gas extraction and processing, liquefaction, liquefied natural gas transportation, regasification, and combustion). Moreover, it is based on data from primary and secondary sources, mainly the Ecoinvent database and the ReCiPe 2016 method. For OCGT, the results showed that the BR-NGF power plant and the natural gas life cycles are responsible for 620.87 gCO2eq./kWh and 178.58 gCO2eq./kWh, respectively. For CCGT, these values are 450.04 gCO2eq./kWh and 129.30 gCO2eq./kWh. Our findings highlight the relevance of the natural gas' life cycle, signaling additional opportunities for reducing the overall carbon footprint of natural gas-fired power generation. [ABSTRACT FROM AUTHOR]

In agriculture, the transformation and drying industries have the most share of energy consumption. The present study aims to optimize the drying process of apple pulp sheets. This process includes checking the energy consumption values and emission of greenhouse gases by the vacuum system in the normal atmosphere (Natm) and controlled atmosphere (Catm) methods. Experiments were conducted at five drying chamber temperatures (i.e., 45, 55, 65, 75, and 85 ℃) and five vacuum pressures (i.e., 40, 50, 60, 70 and 80 kPa). Optimizations were performed using the response surface methodology and the central composite design. The optimum point of apple pulp sheets drying for a gas turbine-oil gas power plant was obtained at a drying temperature of 84.9 ℃ and vacuum pressure of 60.6 kPa. The desirability index was 0.9936 under these conditions. Besides, the optimal value of the response variables, including drying time, total energy consumption (EC), specific energy consumption (SEC), NOX emission, and CO2 emission, were 205 min, 7.65 kWh, 339.02 kWh/kgwater, 1962.94 g/kgwater, and 210,872.40 g/kgwater, respectively. The results showed that in the Catm method, the EC, SEC, NOX, and CO2 from the oil gas turbine power plant were about 19.22%, 37.19%, 37.19%, and 62.72% less than the Natm method, respectively. Therefore, using a vacuum dryer and applying the Catm method in the gas turbine oil of a gas power plant is more economical regarding environmental pollution than the vacuum dryer using the Natm method. [ABSTRACT FROM AUTHOR]

Gas turbines are pivotal in electricity generation and industry, prized for their efficiency and flexibility in meeting diverse power needs. Optimizing their thermal efficiency is essential for improving energy output and sustainability. Soft computing methods such as neural networks, genetic algorithms, and fuzzy logic offer potent tools for this optimization due to their ability to handle the turbines' nonlinear and dynamic characteristics. These techniques facilitate a deeper understanding of the intricate interplay among various parameters affecting thermal performance, thereby enabling the development of intelligent and adaptive turbine systems. By leveraging soft computing, researchers can enhance gas turbine designs to align with modern energy and environmental objectives. This review emphasizes the application of soft computing approaches in analysing and improving gas turbine thermal performance. Such advancements are instrumental in achieving higher energy efficiency, reducing greenhouse gas emissions, and promoting a sustainable energy landscape. Ultimately, integrating soft computing into gas turbine operations promises to advance both technical capabilities and environmental stewardship in the detection of a more resilient and efficient energy infrastructure. [ABSTRACT FROM AUTHOR]

To reduce carbon emissions, generation of electricity from combustion systems is being replaced by renewable resources. However, the most abundant renewable sources – solar and wind – are not dispatchable, vary diurnally and are subject to intermittency, and produce electricity at times in excess of demand (excess production). To manage this variability and capture the excess renewable energy, energy storage technologies are being developed and deployed such as battery energy storage (BES), hydrogen production with electrolyzers (ELY) paired with hydrogen energy storage (HES) and fuel cells (FCs), and renewable natural gas (RNG) production. While BES may be better suited for short duration storage, hydrogen is suited for long duration storage and RNG can decarbonize the natural gas system. California Senate Bill 100 (SB100) sets a goal that all retail electricity sold in the State must be sourced from renewable and zero-carbon resources by 2045, raising the questions of which set of technologies and in what proportion are required to meet the 2045 target in the required timeframe as well as the role of the natural gas infrastructure, if any. To address these questions, this study combines electric grid dispatch modeling and optimization to identify the energy storage and dispatchable technologies in 5-year increments from 2030 to 2045 required to transition from a 60% renewable electric grid in 2035 to a 100% renewable electric grid in 2045. The results show that, by utilizing the established natural gas system to store and transmit hydrogen and RNG, the deployment of battery energy storage is dramatically reduced. The required capacity for BES in 2045, for example, is 40 times lower by leveraging the natural gas infrastructure with a concomitant reduction in cost and associated challenges to transform the electric grid. • The transition to a hydrogen economy can benefit from systematic planning. • Natural gas power plants (NGPPs) facilitate a cost-effective transition. • A high penetration of renewable generation requires long duration storage. • NGPPs significantly reduce the required energy storage capacity. [ABSTRACT FROM AUTHOR]

As the scale of units within virtual power plants (VPPs) continues to expand, establishing an effective operational game model for these internal units has become a pressing issue for enhancing management and operations. This paper integrates photovoltaic generation, wind power, energy storage, and constant-temperature responsive loads, and it also considers micro gas turbines as auxiliary units, collectively forming a typical VPP case study. An operational optimization model was developed for the VPP control center and the micro gas turbines, and the game relationship between them was analyzed. A Stackelberg game model between the VPP control center and the micro gas turbines was proposed. Lastly, an improved D3QN (Dueling Double Deep Q-network) algorithm was employed to compute the VPP's optimal operational strategy based on Stackelberg game theory. The results demonstrate that the proposed model can balance the energy complementarity between the VPP control center and the micro gas turbines, thereby enhancing the overall economic efficiency of operations. [ABSTRACT FROM AUTHOR]

This study aims to assess and examine the gas turbine power generating system at the Qayyarah gas station in Nineveh City, Iraq. The evaluation conducted under real weather conditions for the year 2023. The gas turbine unit has a maximum load output capacity estimated at 125 megawatts. The system comprises a gas turbine, an air compressor, and a generator, all coupled by a shared shaft. Its functions based on a straightforward Joule-Brayton cycle. An analysis of the energy and exergy of the previously mentioned unit, taking into account the principles of conservation of mass, the first law, and the second law. The study specifically focused on examining the impact of several elements, including ambient temperature, compression ratio, relative humidity, and operational load. Two techniques used to replicate company data: the first approach included scripting the simulation using Excel, while the alternative option utilized Aspen HYSYS. The performance of the gas unit evaluated using the average data from each month of the simulation for that year. The simulation findings indicated that the combustion chamber is the primary component responsible for energy dissipation, and the highest energy yield may be achieved from fuel in the form of chemicals. The results of both approaches demonstrated that the maximum energy and energy efficiency achieved around 40.23% and 31.76%, respectively. [ABSTRACT FROM AUTHOR]

This study introduces an Enhanced Autoregressive Integrated Moving Average (E-ARIMA) model for anomaly detection in time-series data, using vibrations monitored by CA 202 accelerometers at the Kirkuk Gas Power Plant as a case study. The objective is to overcome the limitations of traditional ARIMA models in analyzing the non-linear and dynamic nature of industrial sensory data. The novel proposed methodology includes data preparation through linear interpolation to address dataset gaps, stationarity confirmation via the Augmented Dickey-Fuller Test, and ARIMA model optimization against the Akaike Information Criterion, with a specialized time-series cross-validation technique. The results show that E-ARIMA model has superior performance in anomaly detection compared to conventional Seasonal ARIMA (SARIMA) and Vector Autoregressive models. In this regard, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) criteria were utilized for this evaluation. Finally, the most important achievement of this research is that the results highlight the enhanced predictive accuracy of the E-ARIMA model, making it a potent tool for industrial applications such as machinery health monitoring, where early detection of anomalies is crucial to prevent costly downtimes and facilitate maintenance planning. [ABSTRACT FROM AUTHOR]

Natural gas is widely used in many homes for cooking, but a lack of gas leak detection has led to large fires and accidents. This article presents the design and implementation of a natural gas detection and extraction system for domestic kitchens in Lima, Peru. The ESP32 microcontroller allowed remote circuit control, resulting in a more convenient setup than the Arduino UNO microcontroller. After calibration of the sensors and their corresponding programming, three actions were established in response to different gas levels: alarm activation, space ventilation and gas extraction, and thermal shutdown. Strategic sensor placement and improved physical presentation of the system were performed to ensure accurate readings and effective deployment. The results demonstrate the proper functioning of the circuit and its ability to prevent accidents related to gas leaks. The designed system offers the advantage of remote monitoring, providing access to the user from any location. In conclusion, this project offers a comprehensive solution to prevent accidents caused by gas leaks in home kitchens. With satisfactory results in terms of operation and rapid response, the project demonstrates its effectiveness in accident prevention. This design offers a practical and accessible solution, improving security and bringing peace of mind to homes. [ABSTRACT FROM AUTHOR]

There has been a growing recognition in recent years that Electrofuels (e-fuels), also known as renewable and sustainable fuels, which are produced from hydrogen and carbon dioxide as the primary feedstocks, have great potential to reduce carbon footprint and address climate change. In the present study, contrary to most previous studies in which the sources of feedstock's preparation were considered as block boxes, two stand-alone processes are taken into account and incorporated into the e-fuels production system. H 2 is obtained by a high-temperature electrolysis system having an overall efficiency nearly twice that of a low-temperature electrolysis system. CO 2 is captured from a power plant flue gas stream with a purity of 99.81%. Investigating heat integration potential in such an integrated plant is an up-to-date and interesting topic and has not yet been well explored. Therefore, a heat integration within the three individual processes is implemented to reduce energy consumption. The design of the process-to-process heat transfer section is carried out using pinch design rules. By integrating individual units, approximately 42% of the required cold utility, traditionally supplied by chilled water and refrigerants, is allocated to generate high and medium-pressure steams. Having used the high-temperature steam electrolysis for H 2 production, a process that requires less external electricity compared to low-temperature water electrolysis, a power-to-liquid efficiency of 70% is achieved. Furthermore, the results reveal a carbon conversion ratio of 98.6%, indicating the effective converting the carbon from the feedstock into liquid hydrocarbons. [Display omitted] • Heat integration between e-fuels unit and its feedstocks supplier units is done. • Shell-and-tube and plate-fin exchangers are used to design heat exchanger network. • Required cold utilities in the processes decreases by 42% by steam generation. • Power-to-liquid efficiency is improved by 70% through the integration. • Effective converting carbon from the feedstock into liquid hydrocarbons by 98.62%. [ABSTRACT FROM AUTHOR]

This research presents an exergy analysis of a gas turbine power plant situated in Assiut, Egypt, operating under high-temperature conditions. The aim of the study is to assess the performance of the simple gas turbine cycle and identify the sources of thermodynamic inefficiencies using the second law of thermodynamics as a basis for analysis. To accomplish this, a model was developed in EES software utilizing real operational data obtained from the plant's control system. The investigation focused on the impact of varying ambient temperature on the exergy efficiency, exergy destruction, and net power output of the cycle. The results revealed that the combustion chamber accounted for the highest exergy destruction, amounting to 85.22%. This was followed by the compressor at 8.42% and the turbine at 6.36%. The overall energy and exergy efficiencies of the system were determined to be 28.8% and 27.17%, respectively. Furthermore, the study examined the effects of increasing ambient temperature from 0 to 45°C on the system's performance. It was observed that as the temperature rose, the overall exergy efficiency decreased from 27.91 to 26.63%. Simultaneously, the total exergy destruction increased from 126,407 to 138,135 kW. Additionally, the net power output exhibited a decline from 88,084 to 84,051 kW across the same ambient temperature range. These findings highlight the significant influence of ambient temperature on the thermodynamic performance of gas turbine power plants. As temperature rises, a greater amount of exergy is lost, resulting in reduced efficiency and diminished net power output. Therefore, optimizing the design of the combustion chamber is crucial for mitigating the adverse effects of hot weather conditions. The insights obtained from this study can be utilized to enhance the design and operation of gas turbine plants operating in hot climates. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical model of a vapour vacuum system, which is a crucial component of steam power plants of critical importance for energy efficiency. This system consists of three stages, with each stage containing a steam ejector and a gas phase separator in the form of an interstage heat exchanger. The primary purpose of this system is to remove inert gases and maintain the appropriate level of vacuum in the power plant condenser. The presented mathematical model can be used to analyse the operation of the vacuum system in a steady state. Preliminary pressure calculations in various components of the vacuum system show the influence of additional measurement orifice resistance on the vacuum drop in the condenser, which can reduce the efficiency of the entire energy system. It is worth noting that the presented model can be used as a tool for analysing elements of the vacuum system in energy systems. [ABSTRACT FROM AUTHOR]

The potential applications of hydrogen in various fields of the energy sector are attracting attention worldwide, including the use of hydrogen for decarbonizing power systems. In Vietnam, hydrogen is considered to gradually replace natural gas in power generation to achieve the country's net zero target by 2050 but there is a lack of research about this new subject. This study focuses on the computational simulation of the evolution of Vietnam's power system in the period 2030–2050 according to non-conversion and slow, moderate, and accelerated scenarios of natural gas-to-hydrogen conversion at gas turbine power plants. Based on a total power system generation capacity of 150.5 GW in 2030, the modeling results show that the system capacity range of the scenarios is between 568.7 GW and 584.6 GW. In terms of economic performance, the slow conversion scenario has the lowest system cost of USD 1269.0 billion, and the accelerated scenario represents the highest system cost of USD 1283.2 billion. As for CO2 emissions of the power system, the accelerated scenario has the lowest cumulative CO2 emissions in the studied period while the non-conversion appears highest, 2933 and 3212 million tons, respectively. Based on the study results, the possible pathway recommendation of natural gas-to-hydrogen conversion for Vietnam's power system is proposed. [ABSTRACT FROM AUTHOR]

This study investigates the crucial role of Carbon Capture and Storage (CCS) technology in mitigating CO2 emissions from Poland's power systems, which is essential not only for meeting climate targets but also for maintaining energy security in the country. Acknowledging natural gas as a transitional fuel, the focus is on evaluating the decarbonization potential of the natural gas combined cycle (NGCC) power plant. The NGCC with and without an amine-based carbon capture unit was modeled using IPSEpro (SimTech, version 7.0). It was found that the annual CO2 emission from 435.68 MWe (net) NGCC can be reduced from 1,365,501 tons (357.8 kgCO2/MWh) to 136,556 tons (42.9 kgCO2/MWh). On the other hand, the CCS reduced the net electric power of the NGCC from 435.68 MW to 363.47 MW and the net energy efficiency from 55.60% to 46.39%. Nonetheless, these results demonstrate the potential of using the amine-based CO2 capture technology in NGCC systems. This is especially important in the context of the decarbonization of the Polish power system. [ABSTRACT FROM AUTHOR]

The article discusses a solution to the relevant task of analyzing and designing modular buildings made of blocks to be used in industrial and civil engineering. A block that represents a container is a combination of plate and beam systems. The criteria for its failure include both the strength of the individual elements and the loss of stability in a corrugated web. Methods of engineering analysis are hardly applicable to this system. Numerical analysis based on the finite element method is time-consuming, and this fact limits the number of design options for modular buildings made of blocks. Adjustable machine learning models are proposed as a solution to these problems. Decision trees are made and clustered into a single ensemble depending on the values of the design parameters. Key parameters determining the structures of decision trees include design steel resistance values, types of loads and the number of loadings, and ranges of rolled sheet thickness values. An ensemble of such models is used to take into account the nonlinear strain of elements. Piecewise approximation of the dependencies between components of the stress–strain state is used for this purpose. Linear regression equations are subjected to feature binarization to improve the efficiency of nonlinearity projections. The identification of weight coefficients without laborious search optimization methods is a distinguishing characteristic of the proposed models of steel blocks for modular buildings. A modular building block is used to illustrate the effectiveness of the proposed models. Its purpose is to accommodate a gas compressor of a gas turbine power plant. These machine learning models can accurately spot the stress–strain state for different design parameters, in particular for different corrugated web thickness values. As a result, ensemble models predict the stress–strain state with the coefficient of determination equaling 0.88–0.92. [ABSTRACT FROM AUTHOR]

The increasing interest in leveraging green ammonia to mitigate carbon emissions in fertilizer production is paralleled by an expanding acknowledgment of its potential as a fuel for decarbonizing the electricity sector, particularly in high-efficiency gas turbine power plants. Co-firing ammonia with hydrogen presents a promising method for integrating ammonia into existing infrastructures. Within this context, the development of efficient technology for ammonia cracking presents a potential avenue for deploying ammonia in gas turbines. The objective of this study is to conduct a preliminary techno-economic evaluation and uncertainty analysis of two cracking technologies namely a membrane reactor and a conventional FTR (Fired Tubular Reactor) for the co-firing of ammonia with hydrogen in a CCGT (Combined Cycle Gas Turbine) plant. The integration of a membrane reactor during the cracking stage demonstrates a remarkable improvement in the system's thermal efficiency, surpassing traditional approaches by over 25%. Additionally, it brings about an approximate 10% reduction in the levelized cost of hydrogen (LCOH), despite a higher initial capital expenditure (CAPEX). At the CCGT level, the discrepancy in levelized cost of electricity (LCOE) narrows, as it is strongly influenced by the cost of ammonia constituting 80% of the LCOE. Beyond LCOE, the widespread adoption of these systems also faces challenges due to material scarcity. Analysis reveals that revamping just 1 GWe of CCGT assets using membrane reactors would for example necessitate approximately 0.11% of the global palladium supply, and 10% of the global ruthenium production. Considering the limited availability of these resources, coupled with their high demand across multiple sectors and the possibility of external factors such as geopolitical tensions, this strategy seems unfeasible. To tap into this market, future research should prioritize the exploration of alternative membrane materials, such as carbon molecular sieves, and catalysts, like nickel. • Two cracking technologies for NH3–H2 co-firing in CCGT plants have been evaluated. • Membrane reactors reduces H2 cost by 10% and raise efficiency by 25%. • The study indicates the need to explore new materials for this market (e.g. carbon molecular sieve membrane). [ABSTRACT FROM AUTHOR]

Deploying renewable generation to replace conventional fossil-fuel-based energy supplies provides an important pathway for the decarbonization of steelmaking plants. Meanwhile, it is also crucial to improve the flexibility of blast-furnace-gas-fired combined-cycle power plants (BFGCCs) to ease the accommodation of uncertain renewable generation. To this end, this paper proposes the deployment of a molten salt heat storage (MSHS) system in BFGCCs to store the heat of gas turbine flue gas so that the power–heat coupling of these BFGCCs can be unlocked to enhance the flexibility of the energy supply. A stochastic capacity optimization of an integrated BFGCC–MSHS–wind–solar (BMWS) energy system is presented to determine the optimal installed capacities of a BFG holder, MSHS, wind turbine, and PV panel, aiming to achieve an economic and safe energy supply for the entire system. Multiple scenarios considering uncertain fluctuations in load demands and renewable generation are generated with the Monte Carlo method based on a typical scenario. These scenarios are then reduced to representative scenarios using the synchronous substitution and reduction method for stochastic capacity optimization to enhance the reliability of the results. The case study results demonstrate that configuring MSHS reduces the total annualized cost of the BMWS system by 2.28%. Furthermore, considering the uncertainties of the power/heating load and wind/PV generation can reduce the expected annualized total cost of the BMWS system and the corresponding standard deviation by 5.66% and 81.45%, respectively. The BMWS system can achieve 730.68 tons of equivalent CO2 reduction in 24 h due to the successful utilization of renewable energy. This paper provides an effective approach for the decarbonization of energy generation systems in steelmaking plants. [ABSTRACT FROM AUTHOR]

Integrated energy systems (IESs) seek to minimize power generating costs in future power grids through the coupling of different energy technologies. To accommodate fluctuations in load demand due to the penetration of renewable energy sources, flexible operation capabilities must be fully exploited, and even power plants that are traditionally considered as base-load units need to be operated according to unconventional paradigms. Thermomechanical loads induced by frequent power adjustments can accelerate the wear and tear. If a unit is flexibly operated without respecting limits on materials, the risk of failures of expensive components will eventually increase, nullifying the additional profits ensured by flexible operation. In addition to the bounds on power variations (explicit constraints),the solution of the unit dispatch problem needs to meet the limits on the variation of key process variables, including temperature, pressure and flow rate (implicit constraints).The FARM (Feasible Actuator Range Modifier) module was developed to enable existing optimization algorithms to identify solutions to the unit dispatch problem that are both economically favorable and technologically sustainable. Thanks to the iterative dispatcher–validator scheme, FARM permits addressing all the imposed constraints without excessively increasing the computational costs. In this work, the algorithms constituting the module are described, and the performance was assessed by solving the unit dispatch problem for an IES composed of three units, i.e., balance of plant, gas turbine, and high-temperature steam electrolysis. Finally, the FARM module provides dedicated tools for visualizing the response of the constrained variables of interest during operational transients and a tool aiding the operator at making decisions. These techniques might represent the first step towards the deployment of an ecological interface design (EID) for IES units. [ABSTRACT FROM AUTHOR]

This study introduces and evaluates an innovative combined cooling, heating, and power (CCHP) system integrating a gas turbine cycle with transcritical and supercritical CO2 cycles, a high-pressure steam cycle, a Goswami cycle, and a heating terminal. The primary objective is to enhance the thermodynamic efficiency and reduce the environmental impact of power generation. Through detailed exergy and energy analyses, we assessed the system's performance and compared it with traditional energy systems. The methodology included evaluating the irreversibility within each component, particularly highlighting the gas turbine cycle's significant share of irreversibility at 67% and the chamber's highest exergy destruction. Our findings reveal that the integrated system achieves total energy, exergy, and electrical efficiencies of 68.83%, 34.63%, and 33.55%, respectively, while significantly reducing CO2 emissions to 0.298 kgCO2/kWh—outperforming coal, oil, and natural gas power plants in environmental sustainability. Furthermore, the integrated CCHP system showcases superior thermodynamic performance by achieving higher efficiency rates compared to existing solutions detailed in recent studies, thereby marking a significant step forward in the development of sustainable power generation technologies. This research underscores the potential of integrating transcritical and supercritical CO2 cycles with gas turbines to meet energy demands more efficiently and eco-consciously. [ABSTRACT FROM AUTHOR]

AbstractThe focus on optimizing heat exchangers contributes to improved temperature control mechanisms, ensuring the sustainable operation of innovative power plants working toward negative CO2 emissions. In the realm of oxy-combustion within Negative CO2 Emission Power Plants (nCO2PP), the temperature of combustion products surpasses 3000 (K). Addressing this challenge, the imperative arises to reduce these elevated temperatures to a manageable 1100(°C). This critical cooling process is achieved through the injection of water, facilitated by the implementation of heat exchangers. The study delves into the optimization of heat transfer within the heat exchanger pipe, specifically tailored for the context of a nCO2PP. Employing a numerical simulation, the investigation explores the impact of vortex generator geometry, vane angles, single and dual propeller-type swirl generators, and the integration of a novel class of fluid, MXene/water nanofluid. Initially, the study scrutinizes propeller-type geometry at vane angles spanning from 15° to 60°. The enhanced swirl flow associated with lower vane angles leads to improved fluid mixing, fostering more effective heat transfer. Results showed that the 15-degree vane angle, with a wider circumferential coverage, may result in increased wall contact, influencing heat transfer efficiency. Subsequently, at Re = 6000, incremental rates of the Nusselt number (Nun−NusNus%), for θ = 15°, 30°, 45°, and 60° are 175.1, 108.8, 90.7, and 40.3%, respectively. Also, the increment rates of Friction Factor (fnfs) for aforementioned vane angle are 38.48, 9.26, 4.08, and 2.42%, respectively. In addition, for ∅MXene=0.5%, the Nusselt number experiences considerable increments of 22.94, 24.17, 24.70, and 24.707% at Reynolds numbers of 6000, 12,000, 18,000, and 24,000, respectively, compared to pure water, emphasizing the potential of MXene to enhance heat transfer efficiency. [ABSTRACT FROM AUTHOR]

Efficiently converting methane into valuable chemicals via photocatalysis under mild condition represents a sustainable route to energy storage and value‐added manufacture. Despite continued interest in this area, the achievements have been overshadowed by the absence of standardized protocols for conducting photocatalytic methane oxidation experiments as well as evaluating the corresponding performance. In this review, we present a structured solution aimed at addressing these challenges. Firstly, we introduce the norms underlying reactor design and outline various configurations in the gas–solid and gas–solid–liquid reaction systems. This discussion helps choosing the suitable reactors for methane conversion experiments. Subsequently, we offer a comprehensive step‐by‐step protocol applicable to diverse methane‐conversion reactions. Emphasizing meticulous verification and accurate quantification of the products, this protocol highlights the significance of mitigating contamination sources and selecting appropriate detection methods. Lastly, we propose the standardized performance metrics crucial for evaluating photocatalytic methane conversion. By defining these metrics, the community could obtain the consensus of assessing the performance across different studies. Moving forward, the future of photocatalytic methane conversion necessitates further refinement of stringent experimental standards and evaluation criteria. Moreover, development of scalable reactor is essential to facilitate the transition from laboratory proof‐of‐concept to potentially industrial production. [ABSTRACT FROM AUTHOR]

Continuous natural gas flaring wastes significant energy resources and increases greenhouse gas emissions, contributing to global warming. Our work provides an overview of a technique to recover flare gas and reduce CO2 emissions to a minimum level. There are two methods to recover flare gas: the recovery of natural gas liquids and sales gas production by existing LPG unit and the production of liquid fuels by mini-GTL unit (gas to liquid). This study was conducted using real data from the field. All cases were simulated using Aspen HYSYS software. The mini-GTL unit is modeled using an autothermal reforming method. CO2 emissions will be reduced by 107.68 tonne/day in both methods. Economic analyses revealed that the NGL and sales gas product has a net present value (NPV) of 77.03 MMUSD, while the mini-GTL product has an NPV of 73.7 MMUSD. The study showed that we could extract natural gas liquids (NGLs), including propane, LPG, and sales gas, from the flare gas or convert it to liquid products, including gasoline and diesel. The expected internal rate of return (IRR) and payout time (POT) for NGL and sales gas method are 150.73% and 0.27 years, respectively. The mini-GTL method is recommended due to Egypt's petroleum fuel shortage and the best solution without an entry point to the Egyptian national gas grid in the plant. However, the IRR and POT for the mini-GTL method are 30.09% and 1.19 years, respectively, and it needs more CAPEX than the NGL and sales gas method. Highlights: • A model has been developed to recover stranded associated gas through a flare gas recovery system (FGRS). • Natural gas liquids are recovered in addition to sales gas production. • Mini-GTL unit (gas to liquid) is studied as an option for flare gas recovery. • Financial modeling parameters and what-if analysis techniques are reported. [ABSTRACT FROM AUTHOR]

Gas conditioning towers (GCTs) or cooling towers have broad industrial applicability, especially in the cement industry. Most cement factories deal with the same problems faced by other factories. However, the solutions to these problems are barely internationally published and have not received much attention, meaning a lot of time and money is spent on researching previous solutions. Iran is among the top ten cement manufacturing countries in the world. Therefore, many of these problems have occurred in Iranian cement factories. The purpose of this paper is to provide a review of studies in this field over the last couple of decades, with a particular focus on advances in Iran. The most important findings in the literature are summarised in order to suggest the best possible solutions for improving the performance and lifetime of GCTs and decreasing common problems such as dust build-up and wet bottom. Numerical methods for modelling different aspects of GCTs are reviewed. The results can be used to reduce water and energy consumption, air pollution and the repair and maintenance costs of cement factories. [ABSTRACT FROM AUTHOR]

The use of film cooling is crucial to avoid high metal temperatures in gas turbine applications, thus ensuring a high lifetime for vanes and blades. The complex turbulent mixing process between the coolant and the main flow requires an accurate numerical prediction to correctly estimate the impact of ejection conditions on the cooling performance. Recent developments in numerical models aim at using hybrid approaches that combine high precision with low computational cost. This paper is focused on the numerical simulation of a cylindrical film cooling hole that operates at a unitary blowing ratio, with a hot gas Mach number of Ma m = 0.6, while the coolant is characterized by plenum conditions ( Ma c = 0). The adopted numerical approach is the Stress-Blended Eddy Simulation model (SBES), which is a blend between a Reynolds-Averaged Navier–Stokes approach and a modeled Large Eddy Simulation based on the local flow and mesh characteristics. The purpose of this paper is to investigate the ability of the hybrid model to capture the complex mixing between the coolant and the main flow. The cooling performance of the hole is quantified through the film cooling effectiveness, the Net Heat Flux Reduction (NHFR), and the discharge coefficient C D calculation. Numerical results are compared both with the experimental data obtained by the University of Karlsruhe during the EU-funded TATEF2 project and with a Scale Adaptive Simulation (SAS) run on the same computational grid. The use of λ 2 profiles extracted from the flow field allows for isolating the main vortical structures such as horseshoe vortices, counter-rotating vortex pairs (e.g., kidney vortices), Kelvin–Helmholtz instabilities, and hairpin vortices. Eventually, the contribution of the unsteady phenomena occurring at the hole exit section is quantified through Proper Orthogonal Decomposition (POD) and Spectral Proper Orthogonal Decomposition methods (SPOD). [ABSTRACT FROM AUTHOR]

The natural gas (NG)-powered compressors/engines used in liquified natural gas (LNG) plants are a major source of methane emission. The Allam–Fetvedt cycle (AFC), an oxyfuel, carbon-neutral, high-efficiency power plant, generates pipeline-grade CO2. This work performed novel process modeling, economic analysis, and greenhouse gas emissions analysis for a heat-integrated, electrified LNG/AFC/air separation unit (ASU) complex (LAA), then compared it to standalone LNG and AFC/ASU plants (baseline) as well as an LNG plant electrified with AFC/ASU without heat integration. The low-grade heat generated from compressors of the LNG plant can enhance the AFC net power output by 7.1%. Utilizing the nitrogens cold energy reduces the compressor power requirement by 1.6%. In the integrated LAA complex, not only are GHG emissions avoided, but the energy efficiencies are also improved for both the LNG plant and the AFC power plant. A cash flow analysis of LAA was performed over a 20-year period with 5%, 7%, and 10% discount rates and three levels of LNG prices. The 45Q CO2 credit of USD 85/T as stipulated by the recent Inflation Reduction Act (IRA) of 2022 has been incorporated. The results clearly indicate the economic and environmental benefits of the proposed electrification and heat/power integration. [ABSTRACT FROM AUTHOR]

The research considers the design and simulation of 30 000 tons per year of cumene plant using Aspen HYSYS as the simulation tool. The feed (material content) used is the characterized natural gas from Utorogu Gas Field with composition of 0.9019 methane, 0.0694 ethane, 0.0209 propane, 0.00361 n-butane, 0.00414 i-butane, 0.00005 n-pentane and 0.00007 i-pentane. The cumene plant consist of the cryogenic distillation column, de-hydrogenator (Continuous Stirred Tank Reactor), Alkylator (Plug Flow Reactor), storage tanks, separators, pumps, heat exchanger and mixers. The size/design models of basic plant units such as de-hydrogenator, alkylator, separators, storage tanks and distillation column were developed using the principles of mass and energy balance. The result of design or size specifications in terms of equipment diameter of basic plant units obtained from HYSYS simulation are 0.8 m and 0.319 m for the cryogenic and cumene columns; 1.931 m, 2.244 m and 1.366 m for the propane, propene and cumene storage tanks; 1.868 m and 2.076 m for the de-hydrogenator and alkylator; 1.931 m and 2.146 m for propene and cumene separators respectively. The power or capacity of propane and propene pumps configured in the plant are 0.02 kW and 0.005 kW respectively. The result of the reactor and distillation column thickness specification to withstand corrosion, pressure or stress during plant operation in terms of cylindrical shell and ellipsoidal doomed head are 17.36 mm and 4.31 mm respectively. The economic evaluation (overall cost) including the cost of utilities, catalyst, operating, and maintenance for entire plant is $ 3.004 M. [ABSTRACT FROM AUTHOR]

Gas cameras are primarily used to detect gas leaks, but their use has been increasingly extended to mass flow quantification (qOGI). We employ the previously published active illuminated gas camera [Bergau et al. "Real-time active-gas imaging of small gas leaks," J. Sens. Sens. Syst. 12, 61–68 (2023) and Bergau et al. "Flow rate quantification of small methane leaks using laser spectroscopy and deep learning," Process Saf. Environ. Prot. 182, 752–759 (2024)] in a real-world application for quantification, enhancing the camera with two new features: sensitivity adaptation and camera-gas distance detection. This technology was applied to a gas leak found in the pressure swing adsorption room of a biogas plant in Germany. We compare its performance with state-of-the-art quantification gas cameras (qOGI), such as Sensia Mileva 33. Such a comparison between active and passive gas cameras is possible for the first time due to the introduced sensitivity tuning. Additionally, we enclosed the gas leak and measure the methane concentration with a flame ionization detector, providing a gold standard for comparison. Our findings revealed relative offsets to our gold standard of −57% and +319% for the DAS-camera and the Sensia, respectively, suggesting that the accuracy of mass flow quantification could be improved through the use of active gas cameras. [ABSTRACT FROM AUTHOR]

Al-Khirat gas power plant in Iraq experiences significant reductions in power output and efficiency during the hot and dry summer season as an effect of a decrease in air mass flow rate resulting from rising ambient temperatures. To address this issue, upstream cooling systems are implemented and their performance is evaluated through energy and exergy analysis. This study tends to do a comparative study of a gas turbine performance with and without upstream cooling systems with power output, thermal efficiency, exergy efficiency, specific fuel feeding, consumed fuel mass flow rate, and exergy destruction rates. The findings show that upstream cooling systems (UPCS) can effectively mitigate the negative effects of high ambient temperatures on gas turbine performance, especially in Iraq's hot, dry summers. Our study found that higher air temperatures led to a significant drop in gas turbine efficiency, with a 35 K increase in inlet air temperature reducing power production by 27.4%. However, implementing UPCS increased thermal efficiency by 11.5% and exergy efficiency by 11.2%. [ABSTRACT FROM AUTHOR]

This study investigated the energy requirement for running desalination units coupled to cogeneration plants. Various cogeneration systems were explored using power- and heat-allocated approaches. The specific work and heat necessary for operating different desalination systems were determined. The investigation revealed that the specific work and heat remain consistent regardless of the desalination daily capacity. It was observed that the energy demand for operating a desalination system mainly relies on power plant efficiency. The investigation revealed that the energy demand for a plain multi-effect desalination system was lower than that for multi-effect desalination with thermal vapor compression. Additionally, the energy requirement for a multi-effect desalination system with preheaters was lower than that for plain multi-effect desalination. Comparisons also indicated that the energy demand of multi-stage flash exceeds that of different multi-effect desalination systems. Based on the primary thermal energy input, a universal performance ratio was used to evaluate the desalination unit performance. Furthermore, a new correlation was proposed to predict the universal performance ratio. [ABSTRACT FROM AUTHOR]

Maintaining adequate temperatures for preserving food in a domestic refrigerator is a task that is affected by several factors, including the daily use of the appliance. In this sense, this work presents the development of a novel control system based on fuzzy logic that considers usage habits such as the amount of food entering the refrigerator and the frequency of opening doors. Thus, the control comprises input variables corresponding to the internal temperatures of both compartments, the thermal load entered, and the refrigerator door-opening signal. By simulating the usage habits of a refrigerator with a variable-speed compressor, the control performance was evaluated. The results showed that implementing fuzzy control using usage habits was robust enough to maintain adequate thermal conditions within the compartments and a lower thermal fluctuation concerning the reference control of the refrigerator (factory control). In terms of energy, the fuzzy control resulted in an energy saving of 3.20% with the refrigerator empty (without thermal load) compared to the reference control. On the other hand, the individual integration of the thermal load in the fuzzy control resulted in 2.08% energy savings and 5.45% for the integration of the thermal load compared to the reference control. Finally, considering the combination of usage habits, the fuzzy control presented a higher energy consumption than the reference control, around 9.7%. In this case, the fuzzy control maintained more favorable thermal conditions in both compartments, whereas the reference control presented a warmer thermal condition in the freezer. [ABSTRACT FROM AUTHOR]

One of the important parameters in power system stability is frequency. The importance and difficulty of frequency control in a multi-area power system increases with the integration of several power generation sources. One of the important tasks of frequency load control is to eliminate frequency deviation error. The analysis of small signal stability using linearized model of single-area power system with different power sources is presented in this study. Power system area includes three steam turbine power plants with reheater, hydro turbine power plant with transient compensator and gas turbine power plant. To eliminate the frequency deviation that occurs due to small disturbances in the system, a load frequency controller is used. The simulation results along with the analysis of the system modes (eigenvalues analysis) show the dynamic behavior of the system due to the changes in the parameters and the participation coefficients of the power plants. The dynamic behavior of the power system is simulated using MATLAB software. [ABSTRACT FROM AUTHOR]

The highly porous three-dimensional (3D) graphene is a promising solid sorbent for carbon capture and storage. However, generally, the selectivity of a carbon-based sorbent for CO 2 in a gas mixture (such as the post-combustion flue gas in a power plant) is only moderate (∼ 10–20), which limits its applications. Here, using the Grand Canonical Monte Carlo (GCMC) simulation, we investigate a new type of nitrogen doping (N-doping) in graphene that contains cationic nitrogen sites for CO 2 adsorption. We found that due to the favorable electrostatic interaction both CO 2 adsorption and selectivity are improved substantially for the porous 3D graphene with the cationic N-doping and are at least an order of magnitude higher than those for the ones without N-doping or with neutral N-doping (such as graphitic, pyridinic, and pyrrolic ones). Our results highlight the possibility for this modified porous 3D graphene to possess both high selectivity and large adsorption for carbon capture, enhancing its commercial viability. [ABSTRACT FROM AUTHOR]

The Royal Society recently highlighted the huge energy storage needs for the United Kingdom to make stable a grid only fed by solar and wind energy, which must come mostly from hydrogen energy storage. Similar results are obtained for NEOM City modeling the operation of a 50/50 wind and solar energy production, adopting an efficiency for green hydrogen production of 0.75, and an efficiency for power generation from the hydrogen of 0.55. The analysis for NEOM City suggests that with an evenly distributed combined wind and solar generating capacity of 30 GW, a dispatchable annual energy contribution of 60 TWh to the grid is attainable. This presupposes the availability of hydrogen energy storage amounting to no less than 8 TWh and electrolyzers with a power capacity of 22 GW. Ideally, the hydrogen power generation facility should have a nominal power of 13.5 GW. Integrating an intermediate high-efficiency energy storage system, such as flow batteries, between wind and solar generators and electrolyzers can significantly cut the power of the electrolyzers by up to 80 %. A high-efficiency combined cycle gas turbine power plant is then proposed to feed back the grid burning a mixture of green hydrogen and natural gas. By using an almost off-the-shelf technology, gradually shifting towards higher hydrogen contents as green hydrogen becomes more readily available, this solution delivers the lowest possible life cycle CO 2 emission at any time during the progress of the project. NEOM hydrogen city by Bing Image Creator. Generated with AI ∙ 19 November 2023. [Display omitted] [ABSTRACT FROM AUTHOR]