Search Results (322)

Accurate wellbore trajectory prediction is of great significance for enhancing the efficiency and safety of directional drilling in coal mines. However, traditional mechanical analysis methods have high computational complexity, and the existing data-driven models cannot fully integrate non-sequential features such as stratum lithology. To solve these problems, this study proposes a parallel input gated recurrent unit (Pi-GRU) model based on the TensorFlow framework. The GRU network captures the temporal dependencies of sequence data (such as dip angle and azimuth angle), while the BP neural network extracts deep correlations from non-sequence features (such as stratum lithology), thereby achieving multi-source data fusion modeling. Orthogonal experimental design was adopted to optimize the model hyperparameters, and the ablation experiment confirmed the necessity of the parallel architecture. The experimental results obtained based on the data of a certain coal mine in Shanxi Province show that the mean square errors (MSE) of the azimuth and dip angle angles of the Pi-GRU model are 0.06° and 0.01°, respectively. Compared with the emerging CNN-BiLSTM model, they are reduced by 66.67% and 76.92%, respectively. To evaluate the generalization performance of the model, we conducted cross-scenario validation on the dataset of the Dehong Coal Mine. The results showed that even under unknown geological conditions, the Pi-GRU model could still maintain high-precision predictions. The Pi-GRU model not only outperforms existing methods in terms of prediction accuracy, with an inference delay of only 0.21 milliseconds, but also requires much less computing power for training and inference than the maximum computing power of the Jetson TX2 hardware. This proves that the model has good practicability and deployability in the engineering field. It provides a new idea for real-time wellbore trajectory correction in intelligent drilling systems and shows strong application potential in engineering applications. Full article

The internal combustion process in a boiler in power plants has a direct impact on boiler efficiency and NOx generation. The objective of this study is to propose an intelligent deep learning modeling and multi-objective optimization approach that considers NOx emission concentration and boiler thermal efficiency simultaneously for boiler combustion in power plants. Firstly, a hybrid deep learning model, namely, convolutional neural network–bidirectional gated recurrent unit (CNN-BiGRU), is employed to predict the concentration of NOx emissions and the boiler thermal efficiency. Then, based on the hybrid deep prediction model, variables such as primary and secondary airflow rates are considered as controllable variables. A single-objective optimization model based on an improved flow direction algorithm (IFDA) and a multi-objective optimization model based on NSGA-II are developed. For multi-objective optimization using NSGA-II, the average NOx emission concentration is reduced by 5.01%, and the average thermal efficiency is increased by 0.32%. The objective functions are to minimize the boiler thermal efficiency and the concentration of NOx emissions. Comparative analysis of the experiments shows that the NSGA-II algorithm can provide a Pareto optimal front based on the requirements, resulting in better results than single-objective optimization. The effectiveness of the NSGA-II algorithm is demonstrated, and the obtained results provide reference values for the low-carbon and environmentally friendly operation of coal-fired boilers in power plants. Full article

In the context of the intensifying global climate crisis, the power industry, as a significant carbon emitter, urgently needs to promote low-carbon transformation using market mechanisms. In this paper, a multi-objective stochastic optimization scheduling framework for regional power grids integrating carbon trading (CET) and green certificate trading (GCT) is proposed to coordinate the conflict between economic benefits and environmental objectives. By building a deterministic optimization model, the goal of maximizing power generation profit and minimizing carbon emissions is combined in a weighted form, and the power balance, carbon quota constraint, and the proportion of renewable energy are introduced. To deal with the uncertainty of power demand, carbon baseline, and the green certificate ratio, Monte Carlo simulation was further used to generate random parameter scenarios, and the CPLEX solver was used to optimize scheduling schemes iteratively. The simulation results show that when the proportion of green certificates increases from 0.35 to 0.45, the proportion of renewable energy generation increases by 4%, the output of coal power decreases by 12–15%, and the carbon emission decreases by 3–4.5%. At the same time, the tightening of carbon quotas (coefficient increased from 0.78 to 0.84) promoted the output of gas units to increase by 70 MWh, verifying the synergistic emission reduction effect of the “total control + market incentive” policy. Economic–environmental tradeoff analysis shows that high-cost inputs are positively correlated with the proportion of renewable energy, and carbon emissions are significantly negatively correlated with the proportion of green certificates (correlation coefficient −0.79). This study emphasizes that dynamic adjustments of carbon quota and green certificate targets can avoid diminishing marginal emission reduction efficiency, while the independent carbon price mechanism needs to enhance its linkage with economic targets through policy design. This framework provides theoretical support and a practical path for decision-makers to design a flexible market mechanism and build a multi-energy complementary system of “coal power base load protection, gas peak regulation, and renewable energy supplement”. Full article

Enhancing the load-adjustment flexibility of combined heat and power units facilitates the integration of renewable energy and enhances their profitability in dynamic electricity markets. However, the optimal coordination of various retrofitted combined heat and power units to maximize profitability has not been thoroughly investigated. To address this gap, this study conducts thermodynamic analysis and operation optimization for a combined heat and power plant integrated with flexibility retrofits, by developing models for the extraction-condensing unit, high back-pressure retrofitted unit, and low-pressure turbine zero output retrofitted unit. Results show that the low-pressure turbine zero output retrofitted unit achieves the largest energy efficiency (90.7%), while the extraction-condensing unit attains the highest exergy efficiency (38.0%). A plant-level optimization model is proposed to maximize profitability, demonstrating that the retrofitted combined heat and power plant increases total profit by 8.1% (CNY 86.4 million) compared to the original plant (CNY 79.9 million). The profit improvement stems from reduced coal consumption and enhanced heating capacity, enabling better power generation optimization. Furthermore, the study evaluates the profitability under different retrofit combinations. The findings reveal that an optimal profit can be achieved by reasonably coordinating the energy-saving characteristics of high back-pressure units, the heat supply capacity of low-pressure turbine zero output units, and the flexible adjustment capability of extraction-condensing units. Full article

The concept of “material effort” appears in continuum mechanics wherever the response of a material to the currently existing state of loads and boundary conditions loses its previous, predictable character. However, within the material, which still descriptively remains a continuous medium, new physical structures appear and new previously unused physical features of the continuum are activated. The literature is dominated by a simplified way of thinking, which assumes that all these states can be characterized and described by one and the same measure of effort—for metals it is the Huber–Mises–Hencky equivalent stress. Quantitatively, perhaps 90% of the literature is dedicated to this equivalent stress. The remaining authors, as well as the author of this paper, assume that there is no single universal measure of effort that would “fit” all operating conditions of materials. Each state of the structure’s operation may have its own autonomous measure of effort, which expresses the degree of threat from a specific destruction mechanism. In the current energy sector, we are increasingly dealing with “low-cycle thermal fatigue states”. This is related to the fact that large, difficult-to-predict renewable energy sources have been added. Professional energy based on coal and gas units must perform many (even about 100 per year) starts and stops, and this applies not only to the hot state, but often also to the cold state. The question arises as to the allowable shortening of start and stop times that would not to lead to dangerous material effort, and whether there are necessary data and strength characteristics for heat-resistant steels that allow their effort to be determined not only in simple states, but also in complex stress states. Do these data allow for the description of the material’s yield surface? In a previous publication, the author presented the results of tension and compression tests at elevated temperatures for two heat-resistant steels: St12T and 26H2MF. The aim of the current work is to determine the properties and strength characteristics of these steels in a pure torsion test at elevated temperatures. This allows for the analysis of the strength of power turbine components operating primarily on torsion and for determining which of the two tested steels is more resistant to high temperatures. In addition, the properties determined in all three tests (tension, compression, torsion) will allow the determination of the yield surface of these steels at elevated temperatures. They are necessary for the strength analysis of turbine elements in start-up and shutdown cycles, in states changing from cold to hot and vice versa. A modified testing machine was used for pure torsion tests. It allowed for the determination of the sample’s torsion moment as a function of its torsion angle. The experiments were carried out at temperatures of 20 °C, 200 °C, 400 °C, 600 °C, and 800 °C for St12T steel and at temperatures of 20 °C, 200 °C, 400 °C, 550 °C, and 800 °C for 26H2MF steel. Characteristics were drawn up for each sample and compared on a common graph corresponding to the given steel. Based on the methods and relationships from the theory of strength, the yield stress and torsional strength were determined. The yield stress of St12T steel at 600 °C was 319.3 MPa and the torsional strength was 394.4 MPa. For 26H2MH steel at 550 °C, the yield stress was 311.4 and the torsional strength was 382.8 MPa. St12T steel was therefore more resistant to high temperatures than 26H2MF. The combined data from the tension, compression, and torsion tests allowed us to determine the asymmetry and plasticity coefficients, which allowed us to model the yield surface according to the Burzyński criterion as a function of temperature. The obtained results also allowed us to determine the parameters of the Drucker-Prager model and two of the three parameters of the Willam-Warnke and Menetrey-Willam models. The research results are a valuable contribution to the design and diagnostics of power turbine components. Full article

Traditional coal-fired power units are required to improve their operational flexibility to accommodate increasing renewable energy. In this paper, an optimized operation approach of the cold-end system is proposed to improve the flexibility of coal-fired power units. The dynamic models of the cold-end system of a 330 MW coal-fired power unit are developed. The model validation results show that the error between the simulated results and measured values is <3% at the common load range and <5% at the low load range. The applications of cold-end optimization in the load-variation processes with ±3% Pe/min ramps and actual automatic generation control (AGC) response are then studied. The results show that when the back pressure of the unit is relatively low, the cold-end optimization is more effective in improving the ramp-down rate. On the contrary, when the unit operates with relatively high back pressure, this approach is more suitable for improving the ramp-up rate. Moreover, the AGC response quality is noticeably enhanced, which improves the phenomenon of overshooting and reverse regulation. The comprehensive performance indicator K_P increased from 2.27 to 4.63 in the summer scenario, while it increased from 2.08 to 4.34 in the winter scenario. Moreover, the profits under the two scenarios are raised by 39.2% and 42.5%, respectively. The findings of this study are also applicable to supercritical units or other power units with the cold end adopting similar water cooling systems. Future work will incorporate advanced control theories to enhance control robustness, which is critical for the practical implementation of the proposed cold-end optimization approach. Full article

District heating systems in northern China predominantly rely on coal-fired heat sources, necessitating sustainable alternatives to reduce carbon emissions. This study investigates a biomass combined heat and power (CHP) system integrated with cascaded absorption heat pump (AHP) technology to recover waste heat from semi-dry flue gas desulfurization exhaust and turbine condenser cooling water. A multi-source operational framework is developed, coordinating biomass CHP units with coal-fired boilers for peak-load regulation. The proposed system employs a two-stage heat recovery methodology: preliminary sensible heat extraction from non-saturated flue gas (elevating primary heating loop (PHL) return water from 50 °C to 55 °C), followed by serial AHPs utilizing turbine extraction steam to upgrade waste heat from circulating cooling water (further heating PHL water to 85 °C). Parametric analyses demonstrate that the cascaded AHP system reduces turbine steam extraction by 4.4 to 8.8 t/h compared to conventional steam-driven heating, enabling 3235 MWh of annual additional power generation. Environmental benefits include an annual CO₂ reduction of 1821 tonnes, calculated using regional grid emission factors. The integration of waste heat recovery and multi-source coordination achieves synergistic improvements in energy efficiency and operational flexibility, advancing low-carbon transitions in district heating systems. Full article

Under the global context of addressing climate change and actively promoting energy transition, green power has become increasingly vital in the energy structure due to its clean and sustainable advantages. However, the development of green power’s environmental value faces multiple challenges that hinder its marketization. This study first systematically analyzes the current status of developing the environmental value of green power and identifies existing issues. Second, it designs a green power environmental value mechanism and constructs a quantitative model from the perspective of coal-fired power carbon abatement costs, analyzing the emission reduction value of green power in replacing different types of coal-fired power generation. The results show the following: (1) When power generation types are not differentiated, the environmental value exhibits significant seasonal variations. (2) The environmental value for coal-fired units above 300 MW is lower than the overall average, while that of gas-fired units falls between coal-fired units and the average; the environmental value of generating units with a capacity of 300 MW or less is the lowest, followed by that of unconventional coal-fired units. (3) The environmental value calculated based on the marginal carbon abatement cost of coal-fired units, is slightly higher than the tradable green certificate (TGC) price. This study provides policy support for promoting the low-carbon transition of the power sector and facilitating the development of a green power trading market. Full article

Accelerating the low-carbon transition of China’s coal-fired power sector is essential for advancing national sustainability goals and fulfilling global climate commitments. This study introduces an integrated, data-driven analytical framework to facilitate the sustainable transformation of the coal power sector through coordinated unit-level retirements, new capacity planning, and targeted retrofits. By combining a comprehensive unit-level database with a multi-criteria evaluation framework, the analysis incorporates environmental, technical, and economic factors into decision-making for retirement scheduling. Scenario analyses based on the China Energy Transformation Outlook (CETO 2024) delineate both baseline and ideal carbon neutrality pathways. Optimization algorithms are employed to identify cost-effective retrofit strategies or portfolios, minimizing levelized carbon reduction costs. The findings reveal that cumulative emissions can be reduced by 10–14.9 GtCO₂ by 2060, with advanced technologies like CCUS and co-firing contributing over half of retrofit-driven mitigation. The estimated transition cost of 6.2–6.7 trillion CNY underscores the scale of sustainable investment required. Sensitivity analyses further highlight the critical role of reducing green hydrogen costs to enable deep decarbonization. Overall, this study provides a robust and replicable planning tool to support policymakers in formulating strategies that align coal power sector transformation with long-term sustainability and China’s carbon neutrality commitments. Full article

The “carbon neutral power generation” policy of the European Union requires the phasing out of fossil fuel power plants. These plants still play a crucial role in the energy mix in many countries; therefore, efforts are put forward to lower their CO₂ emissions. The available solution for an existing coal plant is the implementation of biomass co-firing, which allows it to reduce twice its carbon footprint in order to achieve the level of natural gas plants, which are preferable on the way to zero-emission power generation. However the side effect is a significant increase in the bulk fuel volumes that are acquired, handled, and finally supplied to the power plant units. A necessary extension of the complex logistic system for unloading, quality tagging, storing, and transporting biomass may increase the plant’s noise emissions beyond the allowed thresholds. For a comprehensive assessment of the concept of expanding the power plant’s biofuel supply system (BSS), a discrete simulation model was built to dimension system elements and verify the overall correctness of the proposed solutions. Then, a dedicated noise emission model was built for the purposes of mandatory environmental impact assessment procedures for the planned expansion of the BSS. The noise model showed the possibility of exceeding the permissible noise levels at night in selected locations. The new simulations of the BSS model were used to analyze various scenarios of biomass supply with regard to alternative switching off the selected branches of the whole BSS. The length of the queue of unloaded freight trains delivering an average quality biomass after a period of 2 weeks is used as a key performance parameter of the BSS. A queue shorter than 1 freight train is accepted. Assuming the rising share of RESS in the Polish energy mix, the thermal plant’s 2-week average power output shall not exceed 70% of its maximum capacity. The results of the simulations indicate that under these constraints, the biofuel supplies can be sufficient regardless of the nighttime stops, if 50% of the supplied biomass volumes are delivered by trucks. If the trucks’ share drops to 25%, the plant’s 2-week average power output is limited to 45% of its maximum power. The use of digital spatial simulation models for a complex, cyclical-continuous transport system to control its operation is an effective method of addressing environmental conflicts at the design stage of the extension of industrial installations in urbanized areas. Full article

The estimation of available active power in coal-fired thermal power plant units involves considerable complexity and remains a critical task for plant operators. To avoid compromising system stability, operators often operate the thermal unit below its full capacity. To address this issue, the aim of this paper is to facilitate the process of estimating the maximum active electrical power by applying an assistive system based on ANFIS (Adaptive Neuro-Fuzzy Inference System), a method that combines the strengths of neural networks and fuzzy logic. Since the generated electric energy is directly linked to the amount of thermal energy produced, the analysis is focused on the boiler combustion process. It has been shown that the key factors in this process are the coal mills and their achievable capacity, as well as the calorific value of coal. Therefore, the proposed assistive system is based on the estimation of the available capacity of each active mill, which is then combined with the estimated calorific value of the coal to determine the achievable active electrical power of the unit. The conducted analysis and experiments confirm the validity of this approach. Full article

To achieve low-carbon economic dispatch and collaborative optimization of carbon capture efficiency in power systems, this paper proposes a flexible carbon capture power plant and generalized energy storage collaborative operation model under a dynamic carbon quota mechanism. First, adjustable carbon capture devices are integrated into high-emission thermal power units to construct carbon–electricity coupled operation modules, enabling a dynamic reduction of carbon emission intensity and enhancing low-carbon performance. Second, a time-varying carbon quota allocation mechanism and a dynamic correction model for carbon emission factors are designed to improve the regulation capability of carbon capture units during peak demand periods. Furthermore, pumped storage systems and price-guided demand response are integrated to form a generalized energy storage system, establishing a “source–load–storage” coordinated peak-shaving framework that alleviates the regulation burden on carbon capture units. Finally, a multi-timescale optimization scheduling model is developed and solved using the GUROBI algorithm to ensure the economic efficiency and operational synergy of system resources. Simulation results demonstrate that, compared with the traditional static quota mode, the proposed dynamic carbon quota mechanism reduces wind curtailment cost by 9.6%, the loss of load cost by 48.8%, and carbon emission cost by 15%. Moreover, the inclusion of generalized energy storage—including pumped storage and demand response—further decreases coal consumption cost by 9% and carbon emission cost by 17%, validating the effectiveness of the proposed approach in achieving both economic and environmental benefits. Full article

To further quantify the role of coal-fired power units in a wind–solar–thermal storage system and improve the construction of clean energy bases, this study examined the temporal production characteristics of wind and solar power and established an operational model for coal-fired power units within a wind–solar–thermal storage system. This approach ensured a stable electricity supply on the basis of power balance. The findings indicate that the correlation between the installed capacity of coal-fired power and the daily power supply capability of energy storage that meets various scheduled power demands can be obtained via the model. As the proportion of wind and solar power in the output power decreases, the influence of the minimum operational load of the coal-fired power units on the curtailment rate intensifies. Notably, the operational cost savings from reducing this minimum operational load surpass those obtained by either downsizing the installed capacity of coal-fired power units or energy storage devices. Among the parameters of this study, the lowest operational cost for the system was observed when wind and solar power generation constituted 76% of the total. This scenario, which ensured stable power output for 95% of the days in a year, had a wind and solar power curtailment rate of 11.3%. Additionally, the energy supplied by storage devices amounted to 1000 MWh, with the ratio of the installed capacity of coal-fired power to the total installed capacities of wind and solar power remaining at 25%. When the ratio of wind and solar power generation to output power was 91%, 76%, and 58%, a 1% reduction in coal consumption by coal-fired units during low-load operation resulted in a decrease in total system operating costs of 0.012%, 0.093%, and 0.089%, respectively. These findings provide valuable data support for the development of clean energy infrastructures. Full article

In the age of widespread renewable energy integration, coal-fired power plants are transitioning from a primary baseload role to a more flexible peak-shaving capacity. Under frequent load changes, the thermal efficiency will significantly decrease. In order to achieve efficient dynamic operation, this study proposes a comprehensive mechanical model of a 300 MW drum-type boiler. Based on the Modelica/DYMOLA platform, the multi-domain equations describing energy and mass balance are programmed and solved. A comprehensive evaluation of the energy transformation within the boiler’s heat exchange components was performed. Utilizing the principles of exergy analysis, this study investigates how fluctuating operational conditions impact the energy dynamics and exergy losses in the drum and heating surfaces. Steady-state simulation reveals that the evaporator and superheater units account for 81.3% of total exergy destruction. Dynamic process analysis shows that the thermal inertia induced by the drum wall results in a significant delay in heat transfer quantity, with a dynamic period of up to 5000 s. The water wall exhibits the highest total dynamic exergy destruction at 9.5 GJ, with a destruction rate of 7.9–8.5 times higher than other components. Full article

Enhancing the operational flexibility and environmental performance of coal-fired boilers under wide-load conditions presents a critical challenge in China’s low-carbon transition, particularly for low-quality coals (LQCs) with abundant reserves, poor combustibility, and high NO_x emissions. To overcome the intrinsically low reactivity of LQC, peak-shaving performance and combustion behavior were systematically investigated on an MW-grade pilot-scale test platform employing the fuel modification strategy in this study. Stable fuel modification was achieved without any auxiliary energy for LQCs and Shenmu bituminous coal (SBC) across a load range of 20~83% and 26~88%, respectively, demonstrating the excellent fuel reactivity and strengthened release control of volatile and nitrogenous species. The modified LQC exhibited ignition, combustion, and burnout characteristics comparable to Shouyang lean coal (SLC), enabling a “dimensionality-reduction utilization” strategy. The double-side fuel modification device (FMD) operation maintained axially symmetric temperatures (<1250 °C) in horizontal combustion chambers, while single-side operation caused thermal asymmetry, with peak temperatures skewed toward the FMD side (<1200 °C). Original NO_x emissions were effectively suppressed, remaining below 106.89 mg/m³ (@6%O₂) for LQC and 122.76 mg/m³ (@6%O₂) for SBC over broad load ranges, and even achieved ultra-low original NO_x emissions (<50 mg/m³). Distinct load-dependent advantages were observed for each coal type: SBC favored high-load thermal uniformity and low-load NO_x abatement, whereas LQC exhibited the inverse trend. These findings underscore the importance of a load-adaptive coal selection and FMD operation mode. This study provides both theoretical insights and engineering guidance for retrofitting coal-fired power units toward flexible, low-emission operation under deep peak-shaving scenarios. Full article