Photocatalytic decomposition of metronidazole by zinc hexaferrite coated with bismuth oxyiodide magnetic nanocomposite: Advanced modelling and optimization with artificial neural network.

The objective of the present study was to employ a green synthesis method to produce a sustainable ZnFe12O19/BiOI nanocomposite and evaluate its efficacy in the photocatalytic degradation of metronidazole (MNZ) from aqueous media. An artificial neural network (ANN) model was developed to predict the performance of the photocatalytic degradation process using experimental data. More importantly, sensitivity analysis was conducted to explore the relationship between MNZ degradation and various experimental parameters. The elimination of MNZ was assessed under different operational parameters, including pH, contaminant concentration, nanocomposite dosage, and retention time. The outcomes exhibited high a desirability performance of the ANN model with a coefficient correlation (R2) of 0.99. Under optimized circumstances, the MNZ elimination efficiency, as well as the reduction in chemical oxygen demand (COD) and total organic carbon (TOC), reached 92.71%, 70.23%, and 55.08%, respectively. The catalyst showed the ability to be regenerated 8 times with only a slight decrease in its photocatalytic activity. Furthermore, the experimental data obtained demonstrated a good agreement with the predictions of the ANN model. As a result, this study fabricated the ZnFe12O19/BiOI nanocomposite, which gave potential implication value in the effective decontamination of pharmaceutical compounds.

Control performance enhancement of gas turbines in the minimum command selection strategy.

Three novel methods, named α, ζ and ϵ, are suggested in this paper to recover the performance loss during switching in the gas turbine control systems. The Minimum Command Selection (MCS) in the gas turbine control systems prompts this performance loss. Any step towards more productivity with less aging factors have a great impact on the gas turbine's lifetime profit and vice versa. Although many hardware upgrades have been studied and applied to accomplish this, in many cases a low-risk manipulation in the software may yield equivalent achievement. State of the art gas turbine control systems are supposed to handle various forms of disturbances, several operation modes and relatively high transients of the gas turbines. The proposed methods dynamically limit the inactive control loop command and utilize the corresponding loop error to optimally switch the loops. The optimality infers a fuzzy choice based on the designated performance criteria. They demonstrate enhanced performance in comparison with conventional techniques such as static or dynamic saturation proportion to active command, integrator fast rewind, and PI tracking mode. An identified model of W251-B2 gas turbine with robust controllers is exploited to evaluate the empirical authenticity. They exhibit superior performance in comparison with traditional MCS and decrease the over-temperature around 9oC[2%], as the load control switches to the temperature control. The proposed methods provide pragmatic and promising tools in the designer's hands to adapt the methods based on the application requirements.

A unified acceptance test framework for power plant gas turbine control systems.

Renovation and retrofit of gas turbine control systems yield significant economic savings, enhanced reliability, and improved performance. In recent years, the gas turbine industry is increasingly facing the need to well-established procedures for the acceptance tests of renovated control systems. This paper proposes a unified framework to evaluate the performance of renovated gas turbine control systems. Under a set of assumptions on the ambient and fuel conditions, a low-complexity modular model is presented and identified using optimization-oriented identification techniques. The accuracy of the proposed model is validated through experimental studies in full-load, min-load, and no-load operating conditions. Subsequently, a model-based analysis framework is proposed to determine realistic levels of tracking performance, robustness margin and disturbance attenuation by utilizing the supporting tools in robust control theory. Quantitative and qualitative performance indices are introduced to provide acceptance criteria for the existing control loops as compared to the optimal ones. The proposed procedure is applied to a W251-B2 gas turbine manufactured by Westinghouse company, and the results show that the optimal control system outperforms the existing controllers based on quantitative and qualitative indices. The proposed procedure determines whether the overall performance of the renovated control system sufficiently meets the requirements.