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While coal–biomass cocombustion has gained considerable attention as a climate-sensitive power generation strategy, the detrimental impact of inorganic species present in low-rank fuels can lead to counterproductive outcomes in combustion systems. Low-rank solid fuels negatively affect heat transfer efficiency and energy conversion during steam generation, resulting in adverse environmental consequences. However, a combination of improved combustion technology and fuel quality improvements can enable the safe utilization of these fuels. This review highlights the pressing need for research in fuel quality improvement as an effective climate change mitigation strategy during the global transition to renewable energy sources. Identifying the problematic species in solid fuels and understanding the mechanisms of ash formation are critical steps toward designing effective strategies to control ash aerosol-induced fouling. The paper discusses methods for promoting clean combustion, including fuel pretreatment and cocombustion. Additionally, the review examines recent advancements in fuel improvement strategies, such as CFD-targeted-in-furnace injection (CFD-TIFI), intelligent soot blowing, and fuel additive technology, with a focus on mineral additives. Due to its high efficiency, low cost, and minimal retrofit requirements, additive technology holds promise for large-scale applications, paving the way for fuel flexibility in utility-scale power generation. The current study emphasizes the importance of fuel ash mineral transformation and characterization techniques for evaluating the effectiveness of fuel additives. Assessing the viability of utility-scale applications economically and the feasibility of a large-scale demonstration requires further research.

The estimation of geomechanical properties such as Young’s modulus from well logs is a crucial task in the field of geomechanical engineering. Traditional methods for estimating these properties can be time-consuming and may not always provide accurate results. In recent years, machine learning techniques have been increasingly used for this purpose, providing a more efficient and accurate alternative. This paper focuses on the estimation of Young’s modulus from well logs using machine learning techniques. The use of well logs as input data for machine learning models has several advantages, including the ability to handle large datasets, the identification of key input features, and the creation of predictive maps. Well logs from well 15/9_F_11A of Volve Field from a depth range 3575–3720 m are used for training the various ML algorithms mainly the support vector regression, linear regression, and decision tree and also for generating the synthetic Young’s modulus. The well 15/9_F_1A of Volve Field is used for predicting Young’s modulus. From the train–test results, decision tree has shown a test score of 0.97. The score metrics are calculated, and the decision tree has a MAPE of 0.034%, which is the least among the other ML algorithms used for prediction. The actual and predicted values of Young’s modulus have shown good precision results. The model shows greater efficiency to analyse large amounts of well log data and presents a cost-effective solution to predict Young’s modulus in wells which contain less information due to financial or technical reasons. Also, the stratigraphy of the training well and testing well is not completely similar, but the model has generated an R2of 0.973 on the test well. Finally, the use of machine learning for estimating Young’s modulus from well logs is proved to be a promising approach that can provide valuable insights and results for geomechanical engineering applications.

The integration of wind–solar energy into hybrid system improves synchronization and lowers power generation variations. It is crucial to consider resource simultaneity when selecting a hybridization location. The current study uses data from 41 years of hourly ERA5 reanalysis to analyze the coexistence of wind and solar energy sources (1979–2019). To take into account the combined impact of simultaneity and capacity factor (CF), as well as to show the viability of establishing a new hybrid power plant for base-load and peak-load applications, a novel hybrid power exploitability index (HPEI) is developed. Additionally, by analyzing the yearly power output gain that may be attained through hybridization, the viability of retrofitting existing freestanding power plants has been examined. According to the findings, resource complementarity abounds in India’s southern region and along its western coast, whereas resource synergy prevails in the northern region. Furthermore, the largest peak CF for wind and sun, respectively, are predicted to be in the southern coastline (51.56%) and northern Himalayan areas (28.48%). In seven of the eight research locations, it has been shown that it is possible to hybridize independent power plants that already exist, with the addition of equal capacity potentially increasing yearly power output by up to 400%. In order to achieve net-zero emissions, hybridizing RE can meet the need for clean energy. Project developers will use the findings of this research to determine where to construct new power plants and where to hybridize existing ones.

Climate models help assess the future availability of wind speeds to extract wind power; however, these climate models are mathematical models that include uncertainty in wind forecasts. Finding an appropriate climate model for analyzing future wind power generation is critical. Therefore, in the present work, six Coordinated Regional Climate Downscaling Experiment-South Asia (CORDEX-SA) climate models and their ensembles were statistically examined in the Indian offshore region using in-situ buoy readings and ERA5 reanalysis data. Statistical parameters like correlation coefficient, bias, RMSD, and standard deviation are computed to examine the applicable model at buoy locations. With ERA5 wind speeds, the overlapping percentage of climate models is later analyzed for the Indian offshore regions. The ensemble model statistically outperforms individual climate models at buoy sites, with 77% overlap with ERA5 wind speeds in the offshore region. Further, the trends and cumulative variations in wind speeds are calculated for ensemble models under two emission scenarios RCP (Representative Concentration Pathway) 4.5 and RCP 8.5. In the future, the North-East (NE) zone will have the most advantageous change in wind speeds (0.21 to 8.68%), whereas the North-West (NW) area will have a negative cumulative change in wind speeds.

[Display omitted] • Systems thinking analysis revealed the deleterious effects of chromium in the environment. • Substantial amounts of Cr(VI) have been extracted from water using GRB. • Surface characterization results confirm the potentiality of the GRB towards Cr(VI). • The sorption mechanism of electrostatic attractions and pore-filling are observed. • Developed GRB adsorbed ∼ 90 % of chromate ions from industrial effluents. Chromium is a toxic heavy metal with a high recalcitrant and carcinogenic nature even at very low concentrations. In the current investigation, systems thinking analysis is adopted to understand the deleterious effects of chromium on the environment. Hazardous chromate ions are effectively been remediated from industrial discharges by employing the robust adsorbent prepared from marine algae, Gracilaria Rhodophyta Biochar. The combined effect of physicochemical factors such as contact time, pH, biochar dosage and initial chromate concentration on adsorption performance is analyzed by adopting the statistical Response Surface Methodology. At the optimum operating conditions of pH: 2–4; GRB dosage: 0.3 g/100 mL; contact time: 90 min; initial chromate concentration: 60 mg/L and temperature: 30 °C ± 2, an amount of 19.6 mg/g of chromate ions are removed by the developed adsorbent. The characterization results showcased the efficiency of biochar towards the adsorption of chromate ions from wastewater. The adsorption phenomenon follows the Langmuir isotherm model and pseudo-second-order rate kinetics. The investigation of selectivity reveals that the developed adsorbent has a good selectivity for Cr(VI) even with the presence of competing ions in the same medium. The developed robust technology can be successfully applied for the adsorption of toxic chromate ions from contaminated industrial samples. [ABSTRACT FROM AUTHOR]

(a) diagrammatic presentation of the photodegradation of MB and CR dyes (blue = MB, red = CR, and (b) electron-hole pairs separation on TiO 2 /CaIn 2 S 4 @rGO. [Display omitted] • Novel TiO 2 /CaIn 2 S 4 @rGO composites (rGO-C1 to rGO-C3) were synthesised for rapid degradation of organic dye. • Among them, the rGO-C 2 exhibited significant photocatalytic degradation activity for MB & CR within 15–30 min. • The LC–MS technique has discovered the degradation pathway of both dyes. • It shows excellent degradation activity for real wastewater from the textile industry and landfill leachate. • Moreover, this catalyst may be reused and stable up to four cycles. The in-situ reduction of GO to rGO and simultaneous deposition of TiO 2 and varying concentrations CaIn 2 S 4 10 % (rGO-C1), 20 % (rGO-C2), and 40 % (rGO-C3) resulted in the formation of robust photocatalysts for the degradation of aqueous (azo and thiazine) dyes methylene blue (MB), congo red (CR) and real wastewater. Batch experiments were performed to analyse the effect of pH, catalyst dose, initial dye concentration and time. Compared to other synthesised composites, rGO-C2 demonstrated enhanced photodegradation activity, with a degradation efficiency of almost 100 % within 25–30 min. A possible mechanism for the photodegradation of dyes was also proposed, and post-degradation products were analysed using the LC–MS technique. The kinetic studies show that the degradation efficiency of rGO-C2 followed pseudo-first-order reaction kinetics (k = 0.027 min−1. The rGO-C2 photocatalysts demonstrated excellent photocatalytic degradation in real textile industry wastewater and landfill leachate wastewater. It displayed excellent recyclability and stability up to the four-cycle. [ABSTRACT FROM AUTHOR]

Economic assets play a key role in the growth of renewable power projects and depend on financial scenarios and policy structures offered by government policies. The focus of the study is to assess the impact of the existing economic scenarios and related government policies on wind and solar power deployment in India. The financial analysis of wind and solar power projects, along with parametric and sensitivity analysis, were performed based on the techno-economic parameters of recent wind and solar energy projects. Factors incorporated into the study are capital cost, annual cash flow, project terminal cost, discount rate, cost of capital, tax rate, capacity factor and technological degradation factor. The results show that the values of levelized cost of electricity (LCOE) calculated for recent wind and solar projects are ₹4.89/kWh ($0.0698/kWh) and ₹4.05/kWh ($0.0578/kWh), respectively, which can be reduced to ₹2.36/kWh ($0.0337/kWh) and ₹2.12/kWh ($0.0303/kWh) through proper amendments to the existing policy, an improved financial framework and technological enhancements. The study will be beneficial to policymakers and project developers, helping to boost the future deployment of wind and solar power projects. (Note: USD ($) was converted to Indian rupee (₹) at an exchange rate of 1$ = 70₹ throughout the paper.)

In the current work, a ZnO–[Zn(CPAMN)] complex-composite material was used as a piezo-photocatalyst for the mineralization of organic dye pollutants and industrial wastewater. The ZnO–[Zn(CPAMN)] complex-composite material showed higher piezo-photocatalytic activity than a simple complex or ZnO. In contrast, the lower bandgap (2.22 eV) makes it easy for the complex-composite to excite photo-generated species under visible light. Instead, the piezoelectric field initiated from the twisting distortion of [ZnO–Zn(CPAMN)] efficiently enhanced the charge-carrier separation and repositioning. Ultrasonic vibration and visible light radiation applied simultaneously to the complex-composite catalyst mineralized 98% of methyl red (MR) and rhodamine B (RhB) dye solutions during the piezo-photocatalytic process within 60 and 80 min, respectively. The reaction MR constant k of the piezo-photocatalysis process was 50 and 20 times higher than those of sole piezocatalysis and photocatalysis, respectively. The pollutants in industrial wastewater samples were effectively mineralized by over 95% compared to the use of simple piezocatalysis and photocatalysis. Compared to precursor materials such as [Zn(CPAMN)] and ZnO, the significant catalytic activity of the ZnO–[Zn(CPAMN)] complex-composite arose because of heterojunction superiority due to the low rate of recombination of active species and the high surface area.