• Wind and Structures
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• v.32 no.1
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• pp.71-80
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• 2021

In steel cable bridges, the use of magnetorheological (MR) dampers between butt cables is constantly increasing to dampen vibrations caused by rain and wind. The biggest problem in the actual applications of those devices is to launch a kind of appropriate algorithm that can effectively and efficiently suppress the perturbation of the tie through basic calculations and optimal solutions. This article discusses the optimal evolutionary design based on a linear and quadratic regulator (hereafter LQR) to lessen the perturbation of the bridges with cables. The control numerical algorithms are expected to effectively and efficiently decrease the possible risks of the structural response in amplification owing to the feedback force in the direction of the MR attenuator. In addition, these numerical algorithms approximate those optimal linear quadratic regulator control forces through the corresponding damping and stiffness, which significantly lessens the work of calculating the significant and optimal control forces. Therefore, it has been shown that it plays an important and significant role in the practical application design of semiactive MR control power systems. In the present proposed novel evolutionary parallel distributed compensator scheme, the vibrational control problem with a simulated demonstration is used to evaluate the numerical algorithmic performance and effectiveness. The results show that these semiactive MR control numerical algorithms which are present proposed in the present paper has better performance than the optimal and the passive control, which is almost reaching the levels of linear quadratic regulator controls with minimal feedback requirements.

• Journal of information and communication convergence engineering
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• v.9 no.5
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• pp.607-609
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• 2011

In this work, we present solutions of two problems. First, the representation of pattern recognition problem in the standard $T_{nml}$ table of the algorithm estimate calculation was considered. Second, the problem of finding the model, consisting of the optimal parameters of an algorithm was considered. Such procedure is carried out by the selection optimal values of the parameters of extreme algorithms. This serves to reduce the number of calculations in the algorithms of estimate calculation and to increase the quality of recognition process. The algorithmic data base of the developed system was based on mathematical apparatus of pattern recognition.

• Membrane and Water Treatment
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• v.15 no.3
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• pp.107-115
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• 2024

This study proposes the recycling of MVS as a value-added product for the removal of phosphate from aqueous solutions. By comparing the phosphate adsorption capacity of each calcined adsorbent at each temperature of MVS, it was determined that the optimal heat treatment temperature of MVS to improve the phosphate adsorption capacity was 800 ℃. MVS-800 suggests an adsorption mechanism through calcium phosphate precipitation. Subsequent kinetic studies with MVS-800 showed that the PFO model was more appropriate than the PSO model. In the equilibrium adsorption experiment, through the analysis of Langmuir and Freundlich models, Langmuir can provide a more appropriate explanation for the phosphate adsorption of MVS-800. This means that the adsorption of phosphate by MVS-800 is uniform over all surfaces and the adsorption consists of a single layer. Thermodynamic analysis of thermally activated MVS-800 shows that phosphate adsorption is an endothermic and involuntary reaction. MVS-800 has the highest phosphate adsorption capacity under low pH conditions. The presence of anions in phosphate adsorption reduces the phosphate adsorption capacity of MVS-800 in the order of CO 3 2-, SO 4 2-, NO 3- and Cl-. Based on experimental data to date, MVS-800 is an environmentally friendly adsorbent for recycling waste resources and is considered to be an adsorbent with high adsorption capacity for removing phosphates from aqueous solutions. This paper combines the advantages of gray predictor and AI fuzzy. The gray predictor can be used to predict whether the bear point exceeds the allowable deviation range, and then perform appropriate control corrections to accelerate the bear point to return to the boundary layer and achieve.