Band 20, Heft 5

Biomass gasification represents a thermal and chemical interaction that transforms biomass substances contained in the reactor. Several interconnected factors influence the gasifier’s performance, including the fuel type, reactor design, and operational parameters. A comprehensive recognizing this operational approach is essential for varied consumers, such as individuals interested in the gasifier’s output, reactor manufacturers aiming to optimize their designs, or planners seeking the best-performing gasifier for specific fuel types. Extensive research and development efforts have been devoted to gasification, encompassing both experimental and computational approaches. Computational modeling tools offer significant advantages, enabling users to determine optimal reactor conditions without time-consuming and costly experimentation. Efficiently interpreting the embedded information in gasification modeling necessitates a systematic and logical analysis, a goal pursued in the present study. Data-driven Support Vector Regression (SVR) based hybrid schemes utilizing Giant Trevally Optimizer (GTO) and Smell Agent Optimization (SAO) were created in this investigation to determine the score of H 2 and N 2 concentration based on the various input parameters. A strong predictive performance of hybrid schemes, especially SVGT, was confirmed by a coefficient of determination ( R 2 ) of 0.994 and 0.999 in the case of yielded N 2 and H 2 estimation. The technique can produce reliable input data for appraisal of costs and benefits and life cycle ecological assessments, allowing politicians and financiers to make more transparent decisions.

With increasing global warming, the focus on sustainable and eco-friendly energy solutions is raised. The production of hydrogen is identified to be extremely important due to its role in efforts to reduce greenhouse gas emissions, hence mitigating climate change. Predicting the amount of hydrogen production accurately in this context is essential. This requires developing and/or applying complex predictive tactics to improve hydrogen production related to reliability and efficiency in thermal power plants. Hydrogen production quantification in thermal power plants using advanced predictive machine learning (ML) tactics is the focus of this exploration. The CatBoost, Random Forest (RF), Long Short-Term Memory (LSTM), and Bootstrap aggregating tactics are used to project hydrogen production. On the other hand, the Firefly algorithm is utilized to fine-tune the hyperparameters of these schemes. Predictions are made on a simulated dataset collected from the recommended power plant, and the accuracy of the schemes’ predictions is assessed using different evaluation metrics. Regarding the results, it turns out that the Firefly-CB model performs very well, having near-perfect R 2 value of 0.9764, proving the capability of predicting hydrogen with small errors. Nevertheless, the Firefly-LSTM model performs poorly with the lowest R 2  = 0.5221 and highest errors during data test. The productivity of Firefly-CB is also compared with that of other advanced optimizers and ensemble schemes, in which Firefly-CB again reveals its strength in the recommended cycle’s hydrogen prediction. These findings from the study demonstrate that hybrid ML schemes can significantly improve the prediction of hydrogen production at power stations, contributing to efficient and sustainable energy management.

This work is aimed to optimize the methylene blue and congo red removal by adsorbent produced by palm fatty acid distillate. The adsorbent was characterized for surface morphology, surface functional groups, elemental composition and textural properties. The adsorption was studied at varying concentrations, contact times, temperatures, and solution pH. The Box-Behnken Design (BBD) combined with the Response Surface Methodology (RSM) was developed on the basis of experimental results to optimize the adsorption conditions. The ANOVA results indicate the high precision of the model to represent the adsorption data as the p-value was < 0.05. Also, the significant R 2 value of greater than 0.9 and large F-value show that the model is valid for the present work The results of the RSM optimization indicate that the optimal conditions to attain a 47.8 mg/g methylene blue capacity are concentration of 100 mg/L, temperature of 70 °C, contact time of 18 h and pH 6, while for congo red, the capacity of 189 mg/g was achieved under the concentration of 300 mg/L, temperature of 70 °C, contact time of 15 h and pH 6. Finally, statistical significance was shown for the linear model, and the current work demonstrates that the adsorbent can be utilized for adsorption of methylene blue and congo red.

The refrigeration absorption cycle is a mature technology to obtain refrigerant effect at low temperatures, and the absorber unit plays a key role on the cycle efficiency. Falling liquid films and packed columns are usually employed in refrigeration cycle with ammonia and water. This study addresses the pros and cons of each equipment, comparing both in terms of absorption capacity, equipment cost and cooling demand by mathematical modelling and simulations. The packed absorption column is modeled in Aspen Plus, and the falling film, by an equation-oriented approach in Matlab. The results indicate that the falling film ensures a uniform coolant distribution, enhancing ammonia absorption efficiency. Consequently, for the same absorption capacity, the falling film enables nearly 80 % reduction in the required volume of the column. However, this space-saving advantage comes at the cost of a 51 % increase in capital and installation expenses. Therefore, the falling film offers a more compact unit but, when space limitation is not an issue, absorption columns might take advantage.

Oil refineries operate in environments of high pressure and temperature. Therefore, maintaining the availability of these assets is a challenge to the maintenance teams. The paper deals with the development of maintenance key performance indicators (KPIs), considering both leading (post-failure) and lagging metrics for maintenance performance. Traditionally, these KPIs are looked upon in isolation; possible relationships between them are not considered. The present study investigated the relationships between various lagging maintenance KPIs relating production rate to equipment availability. The study was conducted under the refinery crude unit, which is generally considered one of the most important parts of any oil refinery. A regression model was developed that empirically relates equipment availability with the production rate. This was further extended to an empirical equation correlating equipment availability and downtime duration with various downtime factors. The results indicate that the availability of equipment has a high correlation with the production rate. This relationship is driven by factors of downtime, which have been sorted according to duration and type. These equations greatly support the management decisions in the development of maintenance strategies. In addition, the research quantifies the costs related to each factor of downtime, which can be applied in formulating future maintenance strategies.

Azaflavanones are natural or synthetic compounds that have shown promising biological activities, including anticancer properties. Pim-1 is a serine/threonine kinase that plays a critical role in various cellular processes, and its overexpression has been associated with several types of cancer, making it an attractive target for anticancer drug development. This study aims to investigate the molecular docking properties of 10 azaflavanone derivative enantiomers against Pim-1 enzymes as a potential anticancer strategy. The docking simulations were carried out with the MOE software’s default settings, with the binding site set to Pim-1’s active site. Binding affinity analysis was performed based on RMSD values, binding energy, and intermolecular interactions such as hydrophobic interaction, π-interaction, and van der Waals for the enantiomers of all compounds. Among the 10 chemical compounds, there are three compounds: 2-(2,5-Dimethylphenyl)-2,3-dihydroquinoline-4(1H)-one, 2-(4-methoxyphenyl)-2,3-dihydroquinoline-4(1H)-one and 2-mesetyl-2,3-dihydroquinoline-4(1H) -one gave good results, with binding energies ranging from −5.7258 to −6.3164 kcal/mol, with RMSD values ranging from 1.0735 to 1.0735 Å. The molecular docking studies demonstrate that azaflavanones derivatives have the potential to serve as promising anticancer inhibitors targeting Pim-1 enzymes.

Consumers evaluate water quality mostly on the basis of its aesthetically acceptable qualities, which include flavor, odor, and appearance. The aesthetic acceptability of water is greatly impacted by the seven heavy metal characteristics: calcium, sodium, potassium, sulfate, magnesium, total hardness, and chloride. These parameters, in turn, have a profound effect on variables such as flavor and odor. The present paper attempts to enhance the level of prediction regarding water quality with the aid of machine learning algorithms. Accordingly, Extra Tree Classification (ETC) was applied to the projection of water quality parameters. In addition to this base model, the Subtraction-Average-Based Optimization and the Horned Lizard Optimization Algorithm were utilized to optimize its level of prediction accuracy. This strategic integration resulted in new hybrid model types. The ETC model, in combination with SABO, is the ETSA framework, which, with HLOA, integrates into the ETHL framework. During Testing for the Accuracy metric, the highest score was 0.986 from the ETHL model, while the ETSA model attained 0.972 and yielded the second best. In the Testing phase, concerning the Precision metric, the best performing model was, once again, the ETHL model with 0.986, while the ETC model developed the weakest performance with a value of 0.970. Future research could enhance water quality prediction through advanced hybrid models, real-time monitoring, deep learning, and validation across diverse water bodies and parameters.

This work highlights a unique control scheme tailored for time-delayed systems under disturbances, blending a fractional filter, dead-time adjustment, and a disturbance compensator. The controller is designed using the Internal Model Control (IMC) approach and optimized through the Snake Optimization (SO) algorithm. This control scheme is applied to practical problems, such as heat exchanger systems and First Order Integrating Plus Delay Time (FOIPDT) process. Integral Absolute Error (IAE) and Integral Square Error (ISE) are key metrics for analyzing closed-loop control performance relative to existing methods. The scheme effectively enhances the closed-loop response by improving IAE values and minimizing control efforts needed to accomplish the preferred output. The Riemann surface principle is utilized for stability analysis. Disturbance rejection and robustness tests show significant improvements in closed-loop response, as illustrated in the numerical simulations.