Synergetic and inhibition effects in carbon dioxide gasification of blends of coals and biomass fuels of Indian origin

Recent updates on remediation approaches of environmentally occurring pollutants using visible light-active nano-photocatalysts

Photocatalysis emerges as a potential remedy for the issue of an unreliable light source. Recognized as the most dependable and potent energy source sustaining life on Earth, sunlight offers a promising solution. Sunlight is abundant and free, operational costs associated with running photocatalytic system using nanoparticles are often lower compared to system relying on artificial light source. The escalating problem of water pollution, particularly in highly industrialized nations, necessitates effective wastewater treatment methods. These methods aim to combat elevated pollution levels, encompassing pharmaceuticals, dyes, flame retardants, and pesticide components. Advanced oxidation processes within photocatalytic wastewater treatment exhibit substantial promise for removing complex organic pollutants. Doped nanomaterials, with their enhanced properties, enable efficient utilization of light. Coupled nanomaterials present significant potential in addressing both water and energy challenges by proficiently eliminating persistent pollutants from environment. Photocatalysis when exposed to sunlight can absorb photons and generate e- h + pairs. This discussion briefly outlines the wastewater treatment facilitated by interconnected nanomaterials, emphasizing their role in water-energy nexus. In exploring the capabilities of components within a functional photocatalyst, a comprehensive analysis of both simple photocatalysts and integrated photocatalytic systems is undertaken. Review aims to provide detailed explanation of the impact of light source on photon generation and significance of solar light on reaction kinetics, considering various parameters such as catalyst dosage, pH, temperature, and types of oxidants. By shedding light on these aspects, this review seeks to enhance our understanding of intricate processes involved in photocatalysis and its potential applications in addressing contemporary environmental challenges.

Co-gasification of biomass and oil shale under CO2 atmosphere: Comparative analysis of fixed-bed reactor, gas chromatography and thermogravimetric analysis coupled with mass spectroscopy (TGA-MS)

Co-gasification of biomass with oil shale offers potential for integrating renewable and fossil energy sources, reducing reliance on fossil fuels. Biomass (pine and birch wood and bark) and oil shale blends (10-30 wt%) were gasified under CO2 conditions using thermogravimetric analysis coupled with mass spectrometry (TGA-MS), fixed-bed reactor, and gas chromatography. Results revealed an interaction between oil shale and biomass, enhancing CO and CH4 concentrations in the producer gas. Bark samples demonstrated higher CO concentrations compared to wood samples, particularly in pine, with 16.1 vol% and 5.4 vol%, respectively. While birch wood showed increased H2 evaporation in TGA-MS experiments, oil shale's impact on H2 concentration was inhibitive, as shown by quantitative analysis. Pine bark, with a threefold catalytic index compared to other biomass samples, demonstrated the highest total gas concentrations (19.2 vol%). Interestingly, pine bark char blends exhibited the lowest surface areas (up to 434 m2/g) among the tested samples.

Investigation of thermal conversion characteristics and performance evaluation of co-combustion of pine sawdust and lignite coal using TGA, artificial neural network modeling and likelihood method

(Co-)combustion of pine sawdust (PS) and lignite coal (LC) were investigated using artificial neural networks (ANN), particle swarm optimization (PSO), and Monte Carlo simulation (MC) as a function of blend ratio, heating rate, and temperature via thermal conversion characteristics. The order of degraded compounds in terms of hemi-cellulosic and lignin-based compounds demonstrated the main oxidation and degradation mechanism of co-combustion of PS and LC. The best prediction (R² of 99.99%) was obtained by ANN28 model. Operating conditions of 90LC10PS, 425 °C, and 19 °C min^-1 were determined by PSO as optimum levels with TG value of 67.5%. Once three-replicated validation experiments were performed under PSO-optimized conditions, mean TG values ware observed as 67.5% with a standard deviation of ±0.4%. Consequently, MC was used to identify the stochastic variability and uncertainty associated with ANN models that were derived to predict TG values.

Steam co-gasification of different ratios of spirit-based distillers' grains and anthracite coal to produce hydrogen-rich gas

In this study, the gas release rate and gas composition in the steam gasification of blends of anthracite coal and spirit-based distillers' grains (SDG) with mass ratios of 3:1, 1:1, and 1:3 were studied. The changes in the gasification reaction activity for different gasification temperatures and sample ratios were investigated, and the synergy between SDG and coal in terms of co-gasification was analysed. The results indicated that the instantaneous release rate of hydrogen was higher than that of other gases for all sample ratios. Upon the addition of SDG, the H₂ content increased while CO and CO₂ contents decreased. The gasification reactivity increased with decreased temperature and ratio of SDG. Furthermore, potassium and calcium in SDG ash played a synergistic catalytic role in the gasification reaction.

Co-gasification of coal and biomass: Synergy, characterization and reactivity of the residual char

The synergy effect between coal and biomass in their co-gasification was studied in a vertical fixed bed reactor, and the physic-chemical structural characteristics and gasification reactivity of the residual char obtained from co-gasification were also investigated. The results shows that, conversion of the residual char and tar into gas is enhanced due to the synergy effect between coal and biomass. The physical structure of residual char shows more pore on coal char when more biomass is added in the co-gasification. The migration of inorganic elements between coal and biomass was found, the formation and competitive role of K₂SiO₃, KAlSiO₄, and Ca₃Al₂(SiO₄)₃ is a mechanism behind the synergy. The graphization degree is enhanced but size of graphite crystallite in the residual char decreases with biomass blending ratio increasing. TGA results strongly suggest the big difference in the reactivity of chars derived from coal and biomass in spite of influence from co-gasification.

Probabilistic uncertainty analysis based on Monte Carlo simulations of co-combustion of hazelnut hull and coal blends: Data-driven modeling and response surface optimization

The aim of present study is to investigate the thermogravimetric behaviour of the co-combustion of hazelnut hull (HH) and coal blends using three approaches: multi non-linear regression (MNLR) modeling based on Box-Behnken design (BBD) (1), optimization based on response surface methodology (RSM) (2), and probabilistic uncertainty analysis based on Monte Carlo simulation as a function of blend ratio, heating rate, and temperature (3). The response variable was predicted by the best-fit MNLR model with a predicted regression coefficient (R²_pred) of 99.5%. Blend ratio of 90/10 (HH to coal, %wt), temperature of 405°C, and heating rate of 44°Cmin^-1 were determined as RSM-optimized conditions with a mass loss of 87.4%. The validation experiments with three replications were performed for justifying the predicted-mass loss percentage and 87.5%±0.2 of mass loss were obtained under RSM-optimized conditions. The probabilistic uncertainty analysis were performed by using Monte Carlo simulations.

Co-combustion of peanut hull and coal blends: Artificial neural networks modeling, particle swarm optimization and Monte Carlo simulation

Co-combustion of coal and peanut hull (PH) were investigated using artificial neural networks (ANN), particle swarm optimization, and Monte Carlo simulation as a function of blend ratio, heating rate, and temperature. The best prediction was reached by ANN61 multi-layer perception model with a R(2) of 0.99994. Blend ratio of 90 to 10 (PH to coal, wt%), temperature of 305°C, and heating rate of 49°Cmin(-1) were determined as the optimum input values and yield of 87.4% was obtained under PSO optimized conditions. The validation experiments resulted in yields of 87.5%±0.2 after three replications. Monte Carlo simulations were used for the probabilistic assessments of stochastic variability and uncertainty associated with explanatory variables of co-combustion process.