Search Results (224)

Shifting towards electrified industrial energy systems is pivotal for meeting global decarbonization objectives, especially since process heat is a significant contributor to greenhouse gas emissions in the industrial sector. This review examines the changing role of heat exchanger networks (HENs) within electrified process industries, where electricity-driven technologies, including electric heaters, steam boilers, heat pumps, mechanical vapour recompression, and organic Rankine cycles, are increasingly supplanting traditional fossil-fuel-based utilities. The analysis identifies key challenges associated with multi-utility integration, multi-pinch configurations, and low-grade heat utilisation that influence HEN design, retrofitting, and optimisation efforts. A comparative evaluation of various methodological frameworks, including mathematical programming, insights-based methods, and hybrid approaches, is presented, highlighting their relevance to the specific constraints and opportunities of electrified systems. Case studies from the chemicals, food processing, and cement sectors demonstrate the practicality and advantages of employing electrified heat exchanger networks (HENs), particularly in terms of energy efficiency, emissions reduction, and enhanced operational flexibility. The review concludes that effective strategies for the design of HENs are crucial in industrial electrification, facilitating increases in efficiency, reductions in emissions, and improvements in economic feasibility, especially when they are integrated with renewable energy sources and advanced control systems. Future initiatives must focus on harmonising technical advances with system-level resilience and economic sustainability considerations. Full article

In this study, we selected the production processes and main products of three typical chemical enterprises in Shanghai, namely SH Petrochemical (part of the oil-refining sector), SK Ethylene, and HS Chlor-Alkali, to quantitatively assess the synergistic effects across technology, policy, and emission mechanisms. The localized air pollutant levels and greenhouse gas emissions of the three enterprises were calculated. The synergistic effects between the end-of-pipe emission reductions for air pollutants and greenhouse gas emissions were analyzed using the pollutant reduction synergistic and cross-elasticity coefficients, including technology comparisons (e.g., acrylonitrile gas incineration (AOGI) technology vs. traditional flare). Based on these data, we used the SimaPro software and the CML-IA model to conduct a life cycle environmental impact assessment regarding the production and upstream processes of their unit products. By combining the life cycle method and the scenario simulation method, we predicted the trends in the environmental impacts of the three chemical enterprises after the implementation of low-carbon development policies in the chemical industry in 2030. We also quantified the synergistic effects of localized air pollutant and greenhouse gas (GHG) emission reductions within the low-carbon development scenario by using cross-elasticity coefficients based on life cycle environmental impacts. The research results show that, for every ton of air pollutant reduced through end-of-pipe treatment measures, the HS Chlor-Alkali enterprise would increase its maximum CO₂ emissions, amounting to about 80 tons. For SK Ethylene, the synergistic coefficient for VOC reduction and CO₂ emissions when using AOGI thermal incineration technology is superior to that for traditional flare thermal incineration. The activities of the three enterprises had an impact on several environmental indicators, particularly the fossil fuel resource depletion potential, accounting for 69.48%, 53.94%, and 34.23% of their total environmental impact loads, respectively. The scenario simulations indicate that, in a low-carbon development scenario, the overall environmental impact loads of SH Petrochemical (refining sector), SK Ethylene, and HS Chlor-Alkali would decrease by 3~5%. This result suggests that optimizing the upstream power structure, using “green hydrogen” instead of “grey hydrogen” in hydrogenation units within refining enterprises, and reducing the consumption of electricity and steam in the production processes of ethylene and chlor-alkali are effective measures in reducing carbon emissions in the chemical industry. The quantification of the synergies based on life cycle environmental impacts revealed that there are relatively strong synergies for air pollutant and GHG emission reductions in the oil-refining industry, while the chlor-alkali industry has the weakest synergies. Full article

The Combined Cycle Combined Heat and Power (CCCHP) systems are an effective way to improve energy efficiency and reduce emissions. This paper examines the energy and environmental impact of CCCHP combustion using waste biomass like the biomass of spent wash (SW), waste crankcase oil (WCO), and bagasse (BA) using an advanced Ebsilon Professional 16 software simulation model. The simulations were designed to achieve 150 MW total power output and 25 MW heating energy. Simulation results indicate that the minimum fuel feed requirement of a 10.762 kg/s flow rate was recorded at the highest calorific value (CV) fuel briquette of 1:8 ratio BA–WCO. The BA–WCO system demonstrates a significantly higher heat recovery capacity in the heat recovery steam generator (HRSG) compared to the BA–SW system. At a 1:8 ratio, it recovers 1463 kJ/kg versus 583 kJ/kg, and 1391 kJ/kg versus 498 kJ/kg at a 1:3 ratio. The CCCHP efficiency was much higher for BA–WCO than those developed from spent wash–bagasse, yielding up to 41.1% compared to a maximum of 26.71%. Furthermore, the BA–WCO system showed a better result than the BA–SW CCCHP system by emitting a low amount of flue gas with low temperature. Full article

A thermodynamic integrated process assessment and experimental evaluation of the conversion of woody biomass to H₂ using chemical looping approaches were explored in this work. Both a two- and three-reactor approach were evaluated for effectiveness with a CaFe₂O₄ oxygen carrier (OC). Experimental test campaigns consisted of semi-batch operations where a single reactor was loaded with a batch charge of the OC and fuel. Multi-reactor approaches were experimentally simulated by switching the gas atmosphere around the batch charge of the OC. The experiments showed that woody biomass was capable of reducing CaFe₂O₄, enabling the production of H₂ from steam oxidation. High steam conversion rates to H₂ of >75% were demonstrated. Reduced CaFe₂O₄ catalyzed tar cracking, multi-cycle tests showed stable reactivity, and sub-pilot-scale tests showed improved reactivity and H₂ yield, accompanied by improved attrition resistance after over 30 cycles. The three-reactor configuration showed the highest potential for H₂ yield between the case studies, while the two-reactor configuration had the lowest auxiliary feed requirement. Both approaches showed increased yields and lower utilities than the baseline steam gasification technology. Full article

The critical consequence of climate change resulting from global warming is the increase in temperature. In combined cycle power plants (CCPPs), the Electric Power Output (PE) is affected by changes in both Ambient Temperature (AT) and Sea Surface Temperature (SST), particularly in plants utilizing seawater cooling systems. As AT increases, air density decreases, leading to a reduction in the mass of air absorbed by the gas turbine. This change alters the fuel–air mixture in the combustion chamber, resulting in decreased turbine power. Similarly, as SST increases, cooling efficiency declines, causing a loss of vacuum in the condenser. A lower vacuum reduces the steam expansion ratio, thereby decreasing the Steam Turbine Power Output. In this study, the effects of increases in these two parameters (AT and SST) due to global warming on the PE of CCPPs are investigated using various regression analysis techniques, Artificial Neural Networks (ANNs) and a hybrid model. The target variables are condenser vacuum (V), Steam Turbine Power Output (ST Power Output), and PE. The relationship of V with three input variables—SST, AT, and ST Power Output—was examined. ST Power Output was analyzed with four input variables: V, SST, AT, and relative humidity (RH). PE was analyzed with five input variables: V, SST, AT, RH, and atmospheric pressure (AP) using regression methods on an hourly basis. These models were compared based on the Coefficient of Determination (R²), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The best results for V, ST Power Output, and PE were obtained using the hybrid (LightGBM + DNN) model, with MAE values of 0.00051, 1.0490, and 2.1942, respectively. As a result, a 1 °C increase in AT leads to a decrease of 4.04681 MWh in the total electricity production of the plant. Furthermore, it was determined that a 1 °C increase in SST leads to a vacuum loss of up to 0.001836 bar_a. Due to this vacuum loss, the steam turbine experiences a power loss of 0.6426 MWh. Considering other associated losses (such as generator efficiency loss due to cooling), the decreases in ST Power Output and PE are calculated as 0.7269 MWh and 0.7642 MWh, respectively. Consequently, the combined effect of a 1 °C increase in both AT and SST results in a 4.8110 MWh production loss in the CCPP. As a result of a 1 °C increase in both AT and SST due to global warming, if the lost energy is to be compensated by an average-efficiency natural gas power plant, an imported coal power plant, or a lignite power plant, then an additional 610 tCO₂e, 11,184 tCO₂e, and 19,913 tCO₂e of greenhouse gases, respectively, would be released into the atmosphere. Full article

This paper aims to evaluate the feasibility and performances of a novel hybrid solar–gas system, which provides electric power and cooling. By using Ebsilon (V15.0) software, the operation, advanced exergic and economic analyses of this hybrid system are conducted. The analysis results show that the total electric power and energy efficiency of the hybrid system are 96.0 MW and 45.8%. The solar energy system contributes an electric power of 9.0 MW. The maximum cooling load is 69.66 MW. The exergic loss and exergic efficiency of the whole hybrid system are 119.1 MW and 44.6%. The combustion chamber (CC) has the maximum exergic loss (56.5 MW). The exergic loss and exergic efficiency of the solar direct steam generator (SDSG) are 28.5 MW and 36.2%. For the air compressor (AC), CC, heat recovery steam generator (HRSG) and refrigeration system (CSS), a considerable part of the exergic loss is exogenous. The avoidable exergic loss of the CC is 11.69 MW. For the SDSG, there is almost no avoidable exergic loss. Economic analysis shows that for the hybrid system, the levelized cost of energy is 0.08125 USD/kWh, and the dynamic recycling cycle is 5.8 years, revealing certain economic feasibility. The results of this paper will contribute to the future research and development of solar–gas hybrid utilization technology to a certain extent. Full article

This paper examines how best to use forest wood for energy application, considering that it is a limited natural resource. Eight systems are considered, including wood stoves, steam systems (boiler, power plant, and combined heat and power (CHP)), and gasification combined systems (gas turbine and combined cycle power plant, CHP, and Fischer–Tropsch). The methodology uses energy analysis and modelling and environmental/sustainability considerations to compare the energy systems. In terms of energy conversion efficiency, steam boilers and high-efficiency wood stoves for heating applications provide the highest efficiencies (~80 to 90%) and should be considered. Steam CHP systems provide lower overall energy conversion efficiencies (~75 to 80%) but do provide some electrical energy, and thus should be considered. The use of wood for the production of electricity on its own should not be considered due to low efficiencies (~20 to 30%). Particulate emissions hinder the application of high-efficiency stoves, especially in urban areas, whereas for industrial-scale steam boilers and CHP systems, particle separators can negate this problem. Gasification/Fischer–Tropsch systems have a lower energy efficiency (~30 to 50%); however, a sustainability argument could be made for liquid fuels that have few sustainable alternatives. Full article

The amount of medical waste is anticipated to increase significantly with population growth. Ineffective medical waste management has resulted in adverse impacts on environmental and human health. Therefore, this study aimed to develop the current medical waste management strategy in Istanbul. GaBi Education version 7.3 was used to conduct a life cycle assessment (LCA) to compare the current medical waste management system (baseline scenario) with alternative scenarios including different proportions of waste disposal methods from an environmental perspective. Global warming, acidification, eutrophication, ozone layer depletion, freshwater aquatic ecotoxicity, and human toxicity were selected as the environmental impact categories found in CML 2001 within GaBi software. Scenarios with a higher proportion of incineration had more negative environmental impact, whereas the scenario incorporating waste segregation/minimization contributed to reducing the environmental impact. Therefore, Scenario 4 (waste segregation at the generation points/waste minimization + incineration + steam sterilization + landfill) presented the best environmental performance with the lowest total environmental impact value of 14.21% among all scenarios and was recommended as the most sustainable alternative for medical waste management in İstanbul. Full article

Modern power generation aims to maximize the extraction of thermal energy from fossil fuels to produce electricity. Combined cycle power plants, leaders in efficiency, sometimes require an additional steam generator to compensate for insufficient exhaust gas energy in the heat recovery steam generator (HRSG), leading to hybrid combined cycles. This study presents a comprehensive thermodynamic analysis of the hybrid combined cycle power plant located in the Valley of Mexico, operating under both full-load and partial-load conditions. The investigation begins with an energy analysis evaluating key performance parameters under real operating conditions, including the power generation, heat flow supply, thermal efficiency, fuel consumption rates, steam flow, and specific fuel consumption. Subsequently, the analysis examines the performance of the steam cycle using the β factor, which quantifies the relationship between heat flows in the steam generator and the HRSG, to maintain a constant steam flow. This evaluation aims to determine the potential utilization of exhaust gas residual energy for partial steam flow generation in the steam turbine. The study concludes with an exergy analysis to quantify the internal irreversibility flows within the system components and determine the overall exergy efficiency of the power plant. The results demonstrate that, under 100% load conditions, the enhanced utilization of exhaust gases from the HRSG leads to fuel savings of 33,903.36 tons annually and increases the exergy efficiency of the hybrid combined cycle power plant to 54.08%. Full article

With the increase in the amount of new energy in new power systems, the response speed of power demand changes in combined cycle gas turbines (CCGTs) is facing new challenges. This paper studies an integrated operation strategy for the coupled molten salt energy storage of CCGT systems, and analyzes the system through simulation calculation. The advantages of the coupled system are determined by comparing the electrical output regulation capability, thermoelectric ratio, gas consumption rate, and peaking capacity ratio. In addition, using stored energy to maintain the temperature of the heat recovery steam generator (HRSG) can shorten the system’s restart time, improve the unit’s operating efficiency, and reduce the start-up cost. Our findings can be used as a reference for accelerating the performance improvement of CCGT systems, which is also crucial in technologies for waste heat recovery, molten salt energy storage technology, and promoting the sustainable development of energy systems. Full article

(1) Background: To enhance the efficiency and minimize the energy consumption of combined cycle power plants (CCPPs), it is crucial to research gas–steam combined cycle (GSCC) performance prediction under various conditions. However, current studies focus more on the subsystems of GSCC, including simpler systems like gas turbines and steam turbines, lacking an overall perspective on the GSCC system as a whole. (2) Methods: this paper focuses on GSCC efficiency prediction, while employing continuous and fluctuating operational data from a CCPP. Specifically, variables from two symmetric gas turbines of the GSCC were employed as model inputs. Deep Neural Network, Simple Recurrent Neural Network, Long Short-Term Memory, and Gated Recurrent Unit (GRU) were tested. Furthermore, the GRU network was employed to evaluate the Plate Heat Exchanger (PHE) installation modification of the CCPP. (3) Results: GRU outperformed the other models, achieving a Mean Absolute Percentage Error of 0.855%. Utilizing multiple variables as model inputs provided the models better accuracy. The evaluation of the CCPP modification indicates that the PHE brought a maximum increase of 7.82 percentage points in combined cycle efficiency. (4) Conclusions: Recurrent Neural Networks, represented by GRU, are capable of predicting GSCC efficiency. Meanwhile, utilizing multiple inputs is essential to GSCC overall performance prediction. The research also proved the PHE to be effective in boosting GSCC thermal efficiency. Full article

The global push for clean hydrogen production has identified nuclear energy, particularly high-temperature gas-cooled reactors (HTGRs), as a promising solution due to their ability to provide high-temperature heat. This study conducted a techno-economic analysis of hydrogen production in Saudi Arabia using the pebble bed modular reactor (HTR-PM), focusing on two methods: high-temperature steam electrolysis (HTSE) and the sulfur–iodine (SI) thermochemical cycle. The Hydrogen Economic Evaluation Program (HEEP) was used to assess the economic viability of both methods, considering key production factors such as the discount rate, nuclear power plant (NPP) capital cost, and hydrogen plant efficiency. The results show that the SI cycle achieves a lower levelized cost of hydrogen (LCOH) at USD 1.22/kg H₂ compared to HTSE at USD 1.47/kg H₂, primarily due to higher thermal efficiency. Nonetheless, HTSE offers simpler system integration. Sensitivity analysis reveals that variations in the discount rate and NPP capital costs significantly impact both production methods, while hydrogen plant efficiency is crucial in determining overall economics. The findings contribute to the broader discourse on sustainable hydrogen production technologies by highlighting the potential of nuclear-driven methods to meet global decarbonization goals. The paper concludes that the HTR-PM offers a viable pathway for large-scale hydrogen production in Saudi Arabia, aligning with the Vision 2030 objectives. Full article

Today, the world is increasingly concerned about energy and environmental challenges, and the search for renewable energy sources has become an unavoidable priority. In this context, Elaeis guineensis (better known as the African oil palm) has been placed in the spotlight due to its great potential and specific characteristics for the production of alternative fuels in the search for sustainable energy solutions. In the present study, bibliometric and co-occurrence analyses are proposed to identify trends, gaps, future directions, and challenges related to the production of bioethanol and hydrogen from oil palm rachis, using VOSviewer v.1.6.20 as a tool to analyze data obtained from SCOPUS. A mapping of several topics related to bioethanol and hydrogen production from oil palm bagasse or rachis is provided, resulting in contributions to the topic under review. It is shown that research is trending towards the use of oil palm rachis as a raw material for hydrogen production, consolidating its position as a promising renewable energy source. The field of hydrogen production from renewable sources has undergone constant evolution, and it is expected to continue growing and playing a significant role in the transition towards cleaner and more sustainable energy sources, potentially involving the adoption of innovative technologies such as solar-powered steam generation. From an economic point of view, developing a circular economy approach to bioethanol and hydrogen production from oil palm rachis and waste management will require innovations in material design, recycling technologies, and the development of effective life cycle strategies that can be evaluated through computer-assisted process simulation. Additionally, the extraction and purification of other gases during the dark fermentation method contribute to reducing greenhouse gas emissions and minimizing energy consumption. Ultimately, the sustainability assessment of bioethanol production processes is crucial, employing various methodologies such as life cycle assessment (LCA), techno-economic analysis, techno-economic resilience, and environmental risk assessment (ERA). This research is original in that it evaluates not only the behavior of the scientific community on these topics over the past 20 years but also examines a less-studied biofuel, namely bioethanol. Full article

Improvement of energy efficiency in technological processes at industrial enterprises is one of the key areas of energy saving. Reduction of energy costs required for the production of energy-intensive products can be achieved through the utilization of waste heat produced by high-temperature thermal furnace units. Generation of electric power based on the waste heat using power cycles with working fluids that are not conventional for large power engineering, may become a promising energy saving trend. In this paper, thermodynamic analysis and optimization of power cycles for the purposes of waste heat recovery are performed. The efficiency of combining several power cycles was also evaluated. It has been established that the combination of the Brayton recompression cycle on supercritical carbon dioxide with the organic Rankine cycle using R124 allows for greater electrical power than steam-power cycles with three pressure circuits under conditions where the gas temperature is in the range of 300–550 °C and the cooling temperature of is up to 80 °C. Additionally, when cooling gases with a high sulfur and moisture content to 150 °C, the combined cycle has greater electrical power at gas temperatures of 330 °C and above. At enterprises where the coolant has a high content of sulfur compounds or moisture and deep cooling of gases will lead to condensation, for example, at petrochemical and non-ferrous metallurgy enterprises, the use of combined cycles can ensure a utilization efficiency of up to 45%. Full article

Combined Cycle Power Plants (CCPPs) generate electrical power through gas turbines and use the exhaust heat from those turbines to power steam turbines, resulting in 50% more power output compared to traditional simple cycle power plants. Predicting the full-load electrical power output ($P_E$) of a CCPP is crucial for efficient operation and sustainable development. Previous studies have used machine learning models, such as the Bagging and Boosting models to predict $P_E$. In this study, we propose employing Super Learner (SL), an ensemble machine learning algorithm, to enhance the accuracy and robustness of predictions. SL utilizes cross-validation to estimate the performance of diverse machine learning models and generates an optimal weighted average based on their respective predictions. It may provide information on the relative contributions of each base learner to the overall prediction skill. For constructing the SL, we consider six individual and ensemble machine learning models as base learners and assess their performances compared to the SL. The dataset used in this study was collected over six years from an operational CCPP. It contains one output variable and four input variables: ambient temperature, atmospheric pressure, relative humidity, and vacuum. The results show that the Boosting algorithms significantly influence the performance of the SL in comparison to the other base learners. The SL outperforms the six individual and ensemble machine learning models used as base learners. It indicates that the SL improves the generalization performance of predictions by combining the predictions of various machine learning models. Full article