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• Ginger rhizome extracts displayed strong cytotoxic effect against HT29, HT116 and MCF-7 cancer cells lines with highest activity against HT29. • Ginger callus extracts recorded non-cytotoxic effect against all examined cancer cells with IC50 >100 µg/mL. • Ginger rhizome extracts contained more phenols and terpenes than the callus ones. • The principal components of ginger; 6-gingerol and 6-shogaol were not detected in callus extracts. • Incidence of genetic changes in DNA of ginger callus during in vitro culture in 2,4-D containing medium. • Most polymorphic bands (11 out of 12) were generated in callus during in vitro culture with total polymorphism 23.07% between callus and plant. Ginger (Zingiber officinale Rosc.) is well known as distinctive spice with considered therapeutic values. In the current work we aimed to investigate cytotoxic effect, phytochemical screening and genetic analysis of ginger callus and their rhizome. Cytotoxicity of ginger rhizome and callus extracts was examined against some human cancer cell lines HT29, HT116 and MCF-7 by MTT method. Phytochemicals were screened by thin layer chromatography (TLC) and genetic analysis was performed by ISSR markers. Ginger rhizome principle components, 6-gingerol and 6-shogoal were used as standards to detect their presence in callus. Results showed that, rhizome extracts displayed a strong cytotoxicity towards all tested cancer cells with the highest activity (IC 50 20.4 ± 3.0 µg/mL; P< 0.05) against HT29. IC 50 of ginger callus extracts were >100 µg/mL in all examined cancer cell lines. TLC chromatograms of rhizome and callus extracts revealed the presence of phenols and terpenes, whereas 6-gingerol and 6-shogaol were only detected in rhizome extracts. Genetic analysis by ISSR markers revealed occurrence some genetic changes in DNA of callus compared to that of rhizome with total polymorphism 23.07%. In conclusion, ginger cytotoxicity to HT29, HT116 and MCF-7 cancer cells was mainly associated with the presence of 6-shogaol and 6-gingerol in extracts, which appeared as strong cytotoxic effect in rhizome compared to non-cytotoxic effect was evaluated in the callus extracts. Therefore, further studies on the callus elicitation and precursors feeding to induce biosynthesis of ginger bioactive molecules are recommended. [ABSTRACT FROM AUTHOR]

Business managers strive to attain the optimal capital structure (OCS), which allows them to raise capital at a minimal cost, thereby maximising their returns. Balancing risk and reward is crucial in determining the target capital structure. Therefore, understanding the optimal leverage ratio and the Speed at which leverage adjustments are made is vital to managers. This study examines the optimal leverage ratio, the speed of adjustment, and the factors contributing to achieving the target capital structure for select 208 steel firms, particularly in an emerging economy like the Indian steel industry. A partial adjustment model is utilised, employing the Generalised Method of Moments (GMM) technique. Additionally, the Altman Z-score is employed to evaluate the financial distress of these steel firms. Very few studies have specifically focused on determining the Speed of adjustment (SOA) using GMM of emerging economies like the Indian steel industry. The findings indicate that steel firms take approximately 2.13 years to reach their target leverage, supporting the existence of the dynamic trade-off theory. The results also highlight the relationship of selected variables (Profitability, Growth, Size, Tangibility, NDTS, Liquidity, and Financial Distress) with the Speed of leverage adjustment and the weak financial position of these businesses. [ABSTRACT FROM AUTHOR]

[Display omitted] • A process simulation study for biohydrogen production is presented. • Optimized steam to biomass ratio reflects higher H 2 production. • LHVgas (60.2 MJ/Nm3) and HHVgas (65.8 MJ/Nm3) is obtained at 600 °C. • Economic analysis depicted $ 2.93 × 106 of proposed total design capital. Biomass gasification is a renewable and sustainable energy source with high product yield and economic viability. This study presents a conceptual model for producing biohydrogen from sunflower husks pellets and waste glycerol using the Aspen Plus® simulation tool. The study analyzes the effects of temperature, steam to biomass rate, and CaO rate on the product gas. The results show that increasing the gasifier temperature from 600 to 750 °C increases H 2 volume percentage to 92%. Optimizing steam to biomass rate and CaO to biomass rate also affects the volume % of H 2 gas in the product gas. The study concludes that a CaO rate of 1.8:1 and a steam-to-biomass ratio of 1.75:1 produce high H 2 content with a high heating value of gas. The total capital cost incurred for the system is $2.93 × 106, with a total operating cost of $14.58 × 106/yr. [ABSTRACT FROM AUTHOR]

• Decarbonizing the steel industry is critical to reaching the goal of global net-zero carbon emissions by 2050. • Encourage the development and implementation of technologies that can reduce carbon emissions significantly in steel manufacturing processes. • Process system engineering perspective for analyzing the critical aspects of steel decarbonization. • To develop and implement energy management systems that optimize energy usage and reduce carbon emissions. Decarbonization of steel manufacturing requires policies to reduce carbon emissions through technology development, renewable energy use, carbon pricing mechanisms, research and development, circular economy practices, energy management systems, and collaboration between industry, government, and academia. This policy assertion seeks to encourage the development and implementation of technologies that can reduce carbon emissions in steel manufacturing processes, such as hydrogen-based steelmaking, carbon capture and utilization, and energy-efficient processes. Low-carbon technologies, renewable energy, a carbon price, material efficiency, and collaboration are key strategies to reduce carbon emissions in the steel sector. Low-carbon energy sources such as wind and solar can be used to power the steelmaking process, while carbon pricing can reduce industrial emissions. To reduce emissions, stakeholders from all stages of the value chain must collaborate to develop decarbonization strategies, such as funding R&D, exchanging knowledge, and offering carbon-cutting incentives. This review provides a conceptual design approach proposed for the successful analysis of steel decarbonization potential from a process system engineering perspective. Challenges and opportunities are also been highlighted with respect to energy, economics, and environmental aspect. Technologies still require more advancement in terms of operation and energy intensity as technical and economic aspects are found superior to conventional technologies. [ABSTRACT FROM AUTHOR]

• Proportional-Integral and Model Predictive Control strategies are investigated. • Dynamic behavior of PI and MPC controllers of a CO 2 removal system is simulated. • Decentralized PID and the MPC controllers have shown similar performances. • MPC controller has moderate overshoots and handle interactions between variables. Control study is crucial to ensure smooth operation of any unit operation for clean energy system. There has been growing attention on the utilization of advanced model-based controllers in control studies for CO 2 capture. However, such advanced controllers are expensive hence proper performance justifications are needed prior to investment. In the current study, both Proportional-Integral (PI) and Model Predictive Control (MPC) control strategies are investigated and implemented on a pilot scale natural gas CO 2 absorption/ stripping system running at higher pressure as well as higher CO 2 content, utilizing MATLAB/SIMULINK-Aspen Plus Dynamics interface. The novelty of the work is mainly on the investigation study between the efficiency of the standard PI and MPC controllers for a high pressure and high CO 2 content pilot-scale CO 2 absorption system. For such high pressure and high CO 2 content system, it is imperative to evaluate whether special focus on the control strategy is needed or not. This work simulates and investigates the dynamic behavior and performances of PI and MPC controllers of a high-pressure absorption/stripping system. In addition, a second order model of the system has been developed, which is useful for future studies. Controller performance of the Proportional-Integral (PI) and Model Predictive Controller (MPC) are compared using the features of the responses as well as the residual error index i.e., Integral Absolute Error (IAE). The performance of the PID and MPC controllers based on set point tracking of both the controlled variables for 5% and 15% step changes have been evaluated. Compared to the PI controller, the MPC controller has no peak overshoots, i.e., it shows 10% and 2.86% lower error in the IAE index, and it is capable of handling interactions between the variables of the absorption/stripping system. [ABSTRACT FROM AUTHOR]

To address climate change, energy security, and waste management, new sustainable energy sources must be developed. This study uses Aspen Plus software to extract bio-H2 from food waste with the goal of efficiency and environmental sustainability. Anaerobic digestion, optimised to operate at 20–25 °C and keep ammonia at 3%, greatly boosted biogas production. The solvent [Emim][FAP], which is based on imidazolium, had excellent performance in purifying biogas. It achieved a high level of methane purity while consuming a minimal amount of energy, with a solvent flow rate of 13.415 m³ /h. Moreover, the utilization of higher temperatures (600–700 °C) during the bio-H 2 generation phase significantly enhanced both the amount and quality of hydrogen produced. Parametric and sensitivity assessments were methodically performed at every stage. This integrated method was practicable and environmentally friendly, according to the economic assessment. H 2 generation using steam reforming results in a TCC of 1.92 × 106 USD. The CO 2 separation step has higher costs (TCC of 2.15 ×107 USD) due to ionic liquid washing and CO 2 liquefaction. Compressor electricity consumption significantly impacts total operating cost (TOC), totaling 4.73 × 108 USD. showing its ability to reduce greenhouse gas emissions, optimize resource utilization, and promote energy sustainability. This study presents a sustainable energy solution that addresses climate and waste challenges. [Display omitted] [ABSTRACT FROM AUTHOR]

Natural gas is the cleanest form of fossil fuel that needs to be purified from CO 2 and H 2 S to diminish harmful emissions and provide feasible processing. The conventional chemical and physical solvents used for this purpose have many drawbacks, including corrosion, solvent loss, high energy requirement, and the formation of toxic compounds, which ultimately disrupt the process and affect the environment. Hybrid solvents have lately been researched to cater to these liabilities and enhance process economics. This study screened eight solvents based on CO 2 selectivity viscosity, absorption enthalpy, corrosivity, working capacity, specific heat, and vapor pressure. From the screened solvents, ten cases of hybrid solvents are simulated and optimized on Aspen HYSYS®. Furthermore, 5Es (Energy, Exergy, Economic, Environmental, and Exergy-environmental) analyses were performed on optimized cases, and results were compared with the base case, MEA (30 wt%). The hybrid blend of Sulfolane and MDEA with weight percentages of 6% and 24%, respectively, showed the highest energy savings of 20% concerning the base case. In addition, it offered 93% savings in exergy destruction and 17.26% in the total operating cost of the process. It is also promising to the environment due to reduced entropy sent to the ecosystem and controlled CO 2 emissions. Therefore, the blend of Sulfolane and MDEA is proposed to Supersede the conventional solvent MEA for the natural gas sweetening process. [Display omitted] • A thorough study on absorption process using hybrid solvents to find a better replacement of conventional MEA process. • Blend of MDEA and Sulfolane had the least energy consumption of about 23 MW. • It also showed the least exergy destruction of 2.5 × 104 kW. • MDEA and Sulfolane blend also showed a satisfactory performance in Environmental analysis with CO 2 emissions. [ABSTRACT FROM AUTHOR]

Purification of Natural gas is vital for utilizing it as a source of energy harvesting for the world. Amine-based chemical absorption technique is the most utilized in the gas field for the purification of gas that ensures the purity of the sweet gas stream with the elimination of carbon dioxide. However, it is considered an energy-intensive process to deal with considerable energy loss and environmental damage to the ecosystem. Five cases have been developed in this study based on various blends comprising mono and tertiary amines in combination with piperazine with a focus on the use of Aqueous Monodiethanolamine (Aq. MDEA), Aqueous Monoethanolamine (Aq. MEA) and piperazine (Pz) for the CO 2 sequestration from the sour natural gas extracted from the remote location located in the province of Baluchistan in Pakistan. The use of exergy, advanced exergy, and exergo environment for optimizing and selecting a suitable solvent combination that may result in an effective separation process has been proposed. Five cases have been developed based on various blends such as mono and tertiary amines combined with piperazine. From the results of all the studied scenarios, Case IV, based on the combination of Aqueous monoethanolamine and piperazine, provides reduced exergy destruction of 2551.7 KW. It was observed that the maximum removal of CO 2 around 99.87 wt% is achieved in case IV. In addition, advance exergy analysis also highlights that case-IV has a venue of 25% exergy destruction avoidable, which would further enhance its performance. Nevertheless, still, case-IV has 75% exergy destruction unavoidable. The environmental factors show that Case-IV has a reduced exergy destruction factor of 0.96, a highly environmentally benign choice as a solvent with a high value of 1.03, and case-IV has the higher operational stability and higher exergy efficiency with an exergy stability value of 0.40. Therefore, monoethanolamine combined with piperazine to be an effective and efficient solvent blend that could be an energy-effective approach for sweetening the natural gas. [Display omitted] • Five cases have been developed based on various blends such as mono and tertiary amines in combination with piperazine. • Case IV, based on the combination of Aqueous monoethanolamine and piperazine, provides reduced exergy destruction of 2551.7 KW with the maximum removal of CO 2 around 99.87 wt% and exergy efficiency of ≥99%. • Advance exergy analysis also highlights that case-IV has a venue of 25% exergy destruction avoidable, which would further enhance its performance. • The exergy analyses were correlated with the environmental factors, which show that case-IV is environmentally more viable based on the exergo-environment factors. [ABSTRACT FROM AUTHOR]

• Experimental investigation of the MIT of aluminium dust using Godbert-Greenwald furnace. • Development of predictive model for MIT using ANNs. • Correlation developed using polynomial surface fitting to predict the MIT. • Effects of dust cloud's concentration and dispersion pressure on MIT. • ANN and PSF approaches for prediction and comparison with the unseen test data set of MIT. The industrial sector continues to face difficulties in preventing dust explosions. Ignition, in particular, is a phenomenon that has yet to be fully comprehended. As a result, safety conditions pertaining to ignition control are rarely assessed to an adequate level. It is generally recognised that the ignition behaviour of combustible dust is influenced by a variety of parameters, including the chemical composition, particle size, moisture content, dispersion pressure, the concentration of dust and so on, but there is still a lack of understanding regarding the simultaneous effect of multiple influential variables. This article aims to provide data on the minimum ignition temperatures of combustible dust using aluminium dust. The minimum ignition temperatures (MITs) were evaluated in a Godbert-Greenwald (GG) furnace with synergistic effects of dispersion pressure and concentrations for two distinct particle size ranges. Based on the statistical nature of the dust explosions and controlling parameters, this study uses data-driven modelling approaches. The experimental data has been divided into the training set and testing set in the proportion of 85% (for training) and 15% (for testing) respectively. A machine learning artificial neural network approach with Levenberg-Marquardt algorithm is implemented to obtain the predictive model for MIT of aluminium dust for both the particle size ranges (100–63 µm, 50–32 µm). The resultant model was obtained with acceptable accuracy in terms of both the training and test data sets. Besides, a statistical surface fit approach has also been adopted to model the MIT to obtain the correlations. It was found that the predictive accuracy is significantly higher for the developed ANN model than the surface fitting based on the minimum values of AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion). The assessment of the MIT for combustible dust is crucial in preventing the ignition and subsequent dust explosion. If a sufficiently accurate estimate of MIT is available then the temperature of the surrounding equipment in the process industries can be controlled well below that particular value. [ABSTRACT FROM AUTHOR]

Coffee arabica L. is an economical crop cultivated in many countries including Yemen. The present study evaluated the phytochemical contents of the methanolic extracts, prepared by maceration, of six Yemeni green coffee beans varieties namely Esmaeli, Hamadi, Harazi, Mattari, Odaini and Yafei. Antioxidant (phosphomolybdenum, antiradical, reducing power and ferrous chelating), and enzyme inhibition activity of the extracts against acetylcholinesterase butyrylcholinesterase, tyrosinase, α-glucosidase, and α-amylase were also studied. Quantification of total phenols revealed that all green beans varieties were richer in their total polyphenolic (63.77–110.98 mg gallic acid equivalent per g of extract) content than their flavonoids one (2.86–5.57 mg rutin equivalent per g of extract). HPLC-MS/MS analysis of 30 selected bioactive compounds showed that all varieties had the same types of phytoconstituents with differences in their relative abundance. Caffeine (1613.89–2466.38 μg/g) followed by 5-caffeoylquinic acid (1017.63–1313.39 μg/g) were the dominant compounds in all varieties with caffeine more abundant in Esmaeli variety while 5-caffeoylquinic acid in Odaini variety. All varieties displayed a pronounced antioxidant property in all the in vitro assays with Odaini variety significantly (p < 0.05) exerted the highest anti -DPPH radicals (253.96 mg Trolox equivalent (TE)/g), metal chelating (19.73 mg Disodium edetate equivalents/g), Cu2+ (441.11 mg TE/g) and Fe3+ (221.04 mg TE/g) reducing activities. Furthermore, the majority of green coffee beans varieties showed comparable enzyme inhibition property with highest activity recorded against tyrosinase (39.35–46.96 mg kojic acid equivalents/g) and acetylcholinesterase (1.80–2.17 mg galanthamine equivalents/g) enzymes. In conclusion, all Yemeni green coffee beans varieties have proven to be rich source of biochemicals with beneficial impact on human health and could be of significant applications in food, pharmaceutical and cosmetics industry. • Six Yemeni green coffee bean varieties were investigated. • Antioxidant and enzyme inhibitory effects were determined. • Caffeine (1613.89–2466.38 μg/g) and 5-caffeoylquinic acid (1017.63–1313.39 μg/g) were the dominant compounds. • Odaini variety exhibited the best antioxidant abilities. • Yemeni coffee beans varieties may be considered as source of nutraceuticals with health-promoting properties. [ABSTRACT FROM AUTHOR]

Abstract: Over the past 70 years, egg yolk (EY) has been continuously used as a cryoprotectant in semen diluents. Earlier studies have shown that the composition of different EYs showed that the basic components were present in similar levels. However, further breakdown and analysis revealed significant differences between EY sources. The objective of this study was to evaluate and compare the effect of substitution chicken EY in ram semen diluents with yolks from other species on in vitro fertilization, subsequent embryonic development and total blastocysts formation. Through the breeding season, ejaculates were collected weekly from five Sarda breed rams of proven fertility, divided equally and diluted in Tris–citrate–fructose–glycerol based diluents containing 20% (v/v) of either partridge, ostrich, turkey, duck, tortoise, or chicken EYs (as a control) at 37°C. Extended semen was cooled to 4°C and preserved as a 0.25mL pellet in LN2. Adults Sarda ewe ovaries were collected from a local slaughterhouse and a total of 913 collected oocytes were divided randomly and matured oocytes were fertilized in vitro with frozen–thawed spermatozoa prepared as described above, and cultured until Day 8. The proportions of cleaved and expanded blastocysts/groups were evaluating after 46, and 144–192h post-insemination (hpi), respectively. Among all sources of EYs, semen cryopreserved in presence of partridge EY significantly increased the proportion of fertilized matured oocytes compared with semen preserved in presence of tortoise or chicken EYs (105/131, 80.1%), (74/123, 60.2%), and (189/270, 70.0%), respectively. Interestingly, using partridge, ostrich, turkey, and duck EYs in ram semen diluents increased significantly (P <0.001) the number of blastocysts formation on Day 6 after IVF versus tortoise or chicken yolks. However, there was no significant difference in total blastocysts yield between all sources of EY. In conclusion, the different compositions of yolks did offer different levels of in vitro production embryos. Specifically, semen diluents in presence of partridge yolks improved cleavage rates, and the numbers of early embryonic development. Whereas using ostrich, turkey, and duck EYs offered comparable fertilization and embryonic development versus conventional semen diluents with 20% chicken EY. [Copyright &y& Elsevier]

• Lignin from industrial crops is the most promising feedstock for chemical energy industry. • ANN and RSM models were applied to the extraction of lignin from oil palm biomass. • The ANN model was superior to RSM for predicting lignin extraction efficiency. • The first report of ANN application for the prediction of physical lignin extraction from oil palm biomass. • The ANN predicted models showed promising results with better accuracy. Lignin from industrial crops is the most promising feedstock which can be used to function modern industrial societies. However, it is very challenging to separate lignin from lignocellulosic biomass effectively. Commercial application of lignin faces many challenges concerning practical applications and sub-optimal extraction approaches. Investigating one factor at a time is a significant limitation in standard experimental protocols. The current processing conditions need to be improved, which can be performed by modelling the processing conditions and identifying the most appropriate process conditions to suit the market demands. In this study, both the response surface methodology (RSM) and an artificial neural network (ANN) model was developed for the enhanced lignin extraction from the available experimental data of our previous work. The effect of various operating parameters such as; extraction temperature, time, particle size range and solid loading affecting the lignin extraction efficiency was optimally analyzed. Likewise, this is the first study reporting a detailed comparison and prediction of lignin extraction using RSM and ANN. The models were evaluated through the coefficient of determination (R 2), Root Means Square Error (RMSE) Mean Average Deviation (MAD) and Average Absolute Relative Error (AARE) showing that the ANN was superior (R 2 = 0.9933, RMSE = 1.129) to the RSM model (R 2 = 0.8805, RMSE = 4.784) for lignin extraction efficiency predictions using various species of oil palm biomass. The results showed the accuracy of the ANN model in the prediction of lignin extraction from empty fruit bunches (EFB), palm mesocarp fibre (PMF) and palm kernel shells (PKS), as compared to the RSM model. [ABSTRACT FROM AUTHOR]

In this work, air gasification of sewage sludge was conducted in a lab-scale bubbling fluidized bed gasifier. Further, the gasification process was modeled using artificial neural networks for the product gas composition with varying temperatures and equivalence ratios. Neural network-based prediction will help to predict the hydrogen production from product gas composition at various temperatures and equivalence ratios. The gasification efficiency and lower heating values were also established as a function of temperatures and equivalence ratios. The maximum H 2 and CO was recorded as 16.26 vol% and 33.55 vol%. Intraileally at ER 0.2 gas composition H 2 , CO, and CH 4 show high concentrations of 20.56 vol%, 45.91 vol%, and 13.32 vol%, respectively. At the same time, CO 2 was lower as 20.20 vol% at ER 0.2. Therefore, optimum values are suggested for maximum H 2 and CO yield and lower concentration of CO 2 at ER 0.25 and temperature of 850 °C. A predictive model based on an Artificial Neural network is also developed to predict the hydrogen production from product gas composition at various temperatures and equivalence ratios. The network has been trained with different topologies to find the optimal structure for temperature and equivalence ratio. The obtained results showed that the regression coefficients for training, validation, and testing are 0.99999, 0.99998, and 0.99992, respectively, which clearly identifies the training efficiency of the trained model. • Sewage sludge air gasification was performed in bubbling fluidized gasifier. • Temperature and ER impact on composition, CGE and LHV of product gas were examined. • Predict the hydrogen production using ANN based modelling. [ABSTRACT FROM AUTHOR]

Vitamin D (Vt. D) is one of the vital hormone having multiple functions in various tissues, including brain. Several evidences reported that Vt. D plays a significant part in memory and cognition as its inadequate amount may accelerate cognitive impairment. This study shows for the first time the antioxidant potential of Vt. D against D-Galactose (D-gal) induced oxidative stress mediated Alzheimer disease (AD) pathology in male adult albino mice. The result reveals that the mice exposed to D-gal (120 mg/kg) for eight weeks have pre-and post-synaptic dysfunction and impaired memory investigated through Morris water maze and Y-maze tests. This is followed by the suppressed Nuclear factor erythroid 2-related factor 2 (NRF2), Heme Oxygenase-1 (HO-1) and elevated expressions of Nuclear Factor kappa B (NF-kB), Tumor Necrosis Factor alpha (TNF-α) and Interleukin 1 beta (IL-1β) proteins in the brain homogenates evaluated through western blotting technique. On the other hand Vt. D (100 μg/kg) administration (three times a week for 4 weeks) activated Silent mating type information regulation 2 homolog 1 (SIRT1) and significantly improved both the neuronal synapse and memory, reduced oxidative stress by upregulating NRF-2 and HO-1 and downregulating NF-kB, TNF-α and IL-1β proteins expression. Most importantly, Vt. D significantly abrogate the amyloidogenic pathway of amyloid beta (Aβ) production against D-gal in the brains of adult male albino mice. These results reveal that Vt. D being an antioxidant agent plays a vital role in reducing the AD pathophysiology in D-gal induced animal model of aging, therefore act as a potential drug candidate in neurodegenerative diseases. Image 1 • D-Galactose induced oxidative stress in male adult albino mice. • D-Galactose caused pre-and post-synaptic and memory dysfunction. • Vitamin D activated SIRT1 to reduce oxidative stress and neuroinflammation. • Vitamin D abrogate amyloidogenic pathway of amyloid beta (Aβ) production. [ABSTRACT FROM AUTHOR]

• Study on air catalytic gasification of PKS using downdraft fixed-bed gasifier. • Effect of temperature, air flowrate, catalyst were evaluated on gas composition. • Time series ANN modelling approach to predict the gaseous flowrate. • Good agreement found amongst developed NARX neural networks model and experiment data. The air gasification of Palm Kernel Shells (PKS) using coal bottom ash (CBA) as a catalyst has been performed in a fixed-bed gasifier. The impact of three process parameters, namely, temperature (575–775°C), air flowrate (1.5–45 litter/min) and catalyst loading (0–30 wt.%) has been investigated on the product gas yield. The composition of the H 2 product is computed to be a maximum of 28 vol.% at 875°C. The air flowrate has a direct relation with H 2 production. The catalysts used have demonstrated a positive impact on the carbon conversion efficiency, showing the increase in carbon-containing gases in the product gas due to the increases in gas yield. A Non-linear Autoregressive Network with exogenous inputs (NARX) neural network has been used to predict the gaseous flowrate dynamically in order to improve gasification performance. The predicted results from the NARX network demonstrate good agreement with the experimental study with R 2 ≥ 0.99. [ABSTRACT FROM AUTHOR]

The dual mixed-refrigeration process makes it possible to achieve higher liquefaction of natural gas with the advancement in both the refrigeration cycles and refrigerant combinations of components. This is made possible by the fact that the process uses multiple mixed refrigerants. This has a significant impact on the overall improved performance of natural gas liquefaction by having a negative influence on the quantity of energy that is consumed. This study proposes a methodology for selecting the MR components based on their thermodynamic behavior in both warm and cold refrigerant streams. As a result, eighteen alternative scenarios are simulated for this study, each based on (i) fixing the warm loop components or (ii) fixing the cold loop components. The procedure was investigated from the point of view of process engineering, with the 3E model of energy, exergy, and economics serving as the decision-making factor. The findings indicate that increasing the number of components for pre-cooling and subcooling cycles from three to five results in specific energy consumption of 0.49 kW.kg LNG −1, which seems to be a reduction of 54% in terms of the amount of energy that is consumed in comparison to the process that is based on three components. The irreversibilities of the process were uncovered by doing an exergy analysis. It identified the cases based on five refrigerant components providing reduced exergy destruction of 3505.02 kW with 59% exergy efficiency. The viability of the proposed process is assessed even further through economic analysis. It was observed that five MR-based processes save 22.93% of the total capital cost, 43.56% of the overall operating cost, and 33.61 %of the total annualized cost. [Display omitted] • Selection and impact of mixed refrigerant on DMR-based NG liquefaction process by varying warm and cold cycle components. • The performance of DMR process based on the choice of mixed refrigerant correlated with the Energy, Exergy, and Economics. • Case A9 had the lowest specific energy usage (0.49 kWh/kg LNG) with 3505.02 kW exergy destruction and 54% efficiency. • Five mixed refrigerant (A9) process saves 23.49% capital, 25% operational, and 23.41% annualized cost. [ABSTRACT FROM AUTHOR]

• Aspen Plus model to produce syngas and bio-hydrogen by varying the composition of the feed stock material for steam gasification process. • Various simulation models were developed with different compositions of biomass feed, including leather and municipal solid waste. • Economic analysis showed that the CAPEX and OPEX of the proposed system is USD 1.42×106 and 2.99×105/yr respectively. This study investigates the potential use of a blend of different types of biomass for sustainable bio-hydrogen production through steam gasification. Simulation models were developed and optimized using Aspen Plus V.11 to achieve optimal bio-hydrogen production while minimizing carbon monoxide production and maintaining a set amount of carbon dioxide concentration in the syngas. The effects of varying the composition of the feedstock material and steam to biomass ratio on hydrogen yield were investigated for five different blends, including leather and municipal solid waste. The study found that the composition of the feedstock played a crucial role in gasification, with higher calorific values for blends containing higher leather content. Additionally, the study showed that specific energy consumption decreased with an increase in total heat duty for four out of the five blends, with Case V having the lowest specific energy consumption of 39.28 kW/kmol of bio H 2. The economic analysis further established the potential for the effective utilization of hybrid biomass for renewable bio-hydrogen production. The optimal case V provides total capital cost of USD 1.42 × 106 and the operating cost of the proposed system accounts to USD 2.99 × 105/yr. The unit production cost involved for the optimal case is USD 0.66/kg of H 2. Overall, this study provides a basis for further investigation of hybrid biomass blends as a source of renewable energy. [ABSTRACT FROM AUTHOR]

[Display omitted] • 3D Concrete Printing is an emerging technology compared to conventional construction. • This review is about a deep understanding of mix design and testing of printed concrete. • Understanding of various rheological and mechanical properties of printable concrete. • Effect of mix constituents on printed concrete with observations from the literature. • Call for establishing universally accepted standards for 3D Printable Concrete. This review paper aims to provide an inclusive characterization of the use of concrete in additive manufacturing by exploring the various parameters that affect the extrudability, pumpability, buildability, thixotropy, interlayer bonding, and anisotropy of concrete in 3D printing. The effects of using different materials, such as cement, supplementary cementitious materials (SCMS), fiber, superplasticizers, accelerators, aggregate, and nano clay, were examined in the concrete mix design. Results show that using cement, SCMS, and fibers in the concrete mix design can significantly affect the concrete's extrudability, pumpability, and buildability. Evaluating the properties of the concrete mixture, both in its fresh and hardened state, is crucial in determining the optimal mix design for 3D concrete printing. This assessment helps ensure that the resulting structure will have the desired performance. However, the long-term durability and sustainability of 3D-printed concrete structures are still uncertain. The paper concludes with a call for further research and development in the field of concrete in additive manufacturing, with a focus on developing a suitable concrete mix design and investigating the long-term durability and sustainability of 3D-printed concrete structures. Additionally, universally accepted standards for 3D-printed concrete structures should be established and implemented to ensure safety and longevity. [ABSTRACT FROM AUTHOR]

The use of soft sensors for the prediction of Nitric Oxides (NOx) emissions to meet quality regulations has become increasingly attractive from the economic point of view. However, implementation of the standard adaptive PLS soft sensors such as the conventional adaptive block-wise recursive PLS (BW-RPLS) and just in time block-wise recursive PLS (JIT-BW-RPLS) to industrial boilers that are not equipped with an in-line hardware analyzer is impractical. This is due to the limited ability of the adaptive soft sensor to recalibrate without feedback from the actual NOx measurement. Hence, in this paper, a PCA-based drift correction method is proposed for an industrial water-tube boiler in which an in-line hardware analyzer is unavailable. The proposed drift correction factor is used to detect when drift happens and subsequently estimate the corrected NOx value to be used in a semi-supervised manner by the conventional BW-RPLS and JIT-BW-RPLS. Both the proposed semi-supervised BW-RPLS and JIT-BW-RPLS with PCA-based drift correction and estimation methods have displayed an additional 10–20% improvement in prediction accuracy relative to the performance of the conventional supervised BW-RPLS method and 50% prediction improvement compared to offline PLS model, during significant drifts in the industrial boiler operation. All the case studies have been performed using actual industrial data of a water-tube boiler. [ABSTRACT FROM AUTHOR]

Fibrous filter made up of non-woven material was utilized in many industrial applications for increasing the collection efficiency and the quality factor. But there exists a competing effect among the fibre diameter, filtration efficiency, pressure drop, and sometime type of aerosol (liquid or solid) plays a crucial role in the performance of the fibrous filter. To avoid overdesigning of the filter along with better performance, optimum set of parameters are to be decided before the manufacturing process. In the current effort, the desirability approach and along with the "Response Surface Methodology (RSM)" were considered to optimize filtration efficiency and pressure drop simultaneously. In this perspective, the impact of Filtration velocity (v), Basis weight (φ), Particle diameter (dp), and Packing fraction (α) on filtration efficiency (η) and pressure drop (Pd) was studied. Based on the outcome, the predicted values lie within experimental data through smart agreement. The maximum percentage (%) error was only 3% and 6% filtration efficiency (η) and pressure drop (Pd), which determine the effectiveness of this useful model. The most dominant factor which affects the filtration efficiency (η) was found to be the Basis weight (φ), followed by packing fraction. However, in the case of pressure drop, the most dominant factors were filtration speed followed by the pachining fraction. Moreover, artificial neural network (ANN) models are developed for the prediction of filtration efficiency and pressure drop. The model accuracy has been estimated by calculating "Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2)". Both models show promising results when compared with experimental data with the R2 value of 98.50–99.86. The optimized values of the maximum filtration efficiency and minimum pressure drop simultaneously were obtained for v = 5, φ = 59.60, dp = 52.23, α = 0.24 according to desirability approach. Droplets migration in coalescing filters. [Display omitted] • RSM was utilized to optimize filtration efficiency and pressure drop simultaneously. • Impact of Filtration velocity (v), Basis weight (φ), Particle diameter (dp), and Packing fraction (α) on filtration efficiency (η) and pressure drop (Pd) was studied. • ANN models are developed for the prediction of filtration efficiency and pressure drop. • The model accuracy has been estimated by calculating "Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (Rs). [ABSTRACT FROM AUTHOR]

Polysulfone (PSF) based mixed matrix membranes (MMMs) are one of the most broadly studied polymeric materials used for CO 2 /CH 4 separation. The performance of existing PSF membranes encounters a bottleneck for widespread expansion in industrial applications due to the trade-off amongst permeability and selectivity. Membrane performance has been postulated to be enhanced via functionalization of filler at different weight percentages. Nonetheless, the preparation of functionalized MMMs without defects and its empirical study that exhibits improved CO 2 /CH 4 separation performance is challenging at an experimental scale that needs prior knowledge of the compatibility between the filler and polymer. Molecular simulation approaches can be used to explore the effect of functionalization on MMM's gas transport properties at an atomic level without the challenges in the experimental study, however, they have received less scrutiny to date. In addition, most of the research has focused on pure gas studies while mixed gas transport properties that reflect real separation in functionalized silica/PSF MMMs are scarcely available. In this work, a molecular simulation computational framework has been developed to investigate the structural, physical properties and gas transport behavior of amine-functionalized silica/PSF-based MMMs. The effect of varying weight percentages (i.e., 15–30 wt.%) of amine-functionalized silica and gas concentrations (i.e., 30% CH 4 /CO 2 , 50% CH 4 /CO 2 , and 70% CH 4 /CO 2) on physical and gas transport characteristics in amine-functionalized silica/PSF MMMs at 308.15 K and 1 atm has been investigated. Functionalization of silica nanoparticles was found to increase the diffusion and solubility coefficients, leading to an increase in the percentage enhancement of permeability and selectivity for amine-functionalized silica/PSF MMM by 566% and 56%, respectively, compared to silica/PSF-based MMMs at optimal weight percentage of 20 wt.%. The model's permeability differed by 7.1% under mixed gas conditions. The findings of this study could help to improve real CO 2 /CH 4 separation in the future design and concept of functionalized MMMs using molecular simulation and empirical modeling strategies. [Display omitted] • Molecular simulation computational framework to simulate amine-functionalized silica/PSF-based mixed matrix membranes. • Elucidation of the MMMs for CO 2 /CH 4 gas separation under varying filler weight percentages and mixed gas concentrations. • Optimal weight percentage at 20 wt.% amine-functionalized silica in PSF MMM. • Improvement in gas transport properties by 566% and 56% for permeability and selectivity. • Development of modified parallel resistance empirical model for the MMMs with error of 7.1%. [ABSTRACT FROM AUTHOR]

[Display omitted] • Machine learning models can accurately model thermal conversion methods. • Classification, regression, and optimization are involved in thermal conversion. • Artificial neural networks have been the most commonly employed algorithm. • Optimization methods were used for ANN network selection and hyper-parameters. • Hybrid and novel ML algorithms (deep learning) with large databases are expected. Thermochemical conversion of biomass has been considered a promising technique to produce alternative renewable fuel sources for future energy supply. However, these processes are often complex, labor-intensive, and time-consuming. Significant efforts have been made in developing strategies for modeling thermochemical conversion processes to maximize their performance and productivity. Among these strategies, machine learning (ML) has attracted substantial interest in recent years in thermochemical conversion process optimization, yield prediction, real-time monitoring, and process control. This study presents a comprehensive review of the research and development in state-of-the-art ML applications in pyrolysis, torrefaction, hydrothermal treatment, gasification, and combustion. Artificial neural networks have been widely employed due to their ability to learn extremely non-linear input–output correlations. Furthermore, the hybrid ML models outperformed the traditional ML models in modeling and optimization tasks. The comparison between various ML methods for different applications, and insights about where the current research is heading, is highlighted. Finally, based on the critical analysis, existing research knowledge gaps are identified, and future recommendations are presented. [ABSTRACT FROM AUTHOR]

• Agro-industrial residues are excellent pyrolysis feedstocks. • TG-DTA is a promising technique to study the thermal decomposition of residues. • Coats-Redfern is successfully applied to slow pyrolysis using five reaction mechanisms. • The ascending order of the activation energy: corncob > rice husk > wood chips > wheat straw > bagasse. Agro-industrial residue is widely considered as a rich source of energy, with varying characteristics depending on the geographical region or origin from where it is collected. Rice husk, bagasse, corncob, wheat straw and wood chips do not find many applications in Pakistan. As they are available in large quantities and at lower cost, therefore it makes them a favorable candidate for bioenergy. In this study, five agro-industrial residues, of Pakistani origin, were thermally degraded in the absence of air and at a constant heating rate of 5 °C min−1. Kinetics of the pyrolysis process was performed using Coats-Redfern method at five reaction mechanisms. Corncob was found to degrade at lower temperature with fastest rate as compared to all the other wastes. The kinetic parameters obtained from Coats-Redfern method were used to evaluate the thermodynamic behavior of these wastes and afterwards a comparison was drawn. Based on the ascending order of the activation energy, the residues can be classified in terms of preference as corncob > rice husk > wood chips > wheat straw > bagasse. [ABSTRACT FROM AUTHOR]