Corrosion test data were measured using non-destructive electrochemical techniques and analysed for studying inhibition effectiveness by different concentrations of NazCr207 on the corrosion of concrete steel-rehar in...Corrosion test data were measured using non-destructive electrochemical techniques and analysed for studying inhibition effectiveness by different concentrations of NazCr207 on the corrosion of concrete steel-rehar in NaC1 and in H2SO4 media. For these, specifications of ASTM G16-95 R04 were combined with the normal and the Gumbel probability density functions as model analytical methods for addressing issues of conflicting reports of inhibitor effectiveness that had generated concerns. Results show that reinforced concrete samples admixed with concentrations having 4 g （0.012 7 tool）, 8 g （0.025 4 mol） and 6 g （0.019 l tool） NaaCr207 exhibited, in that order, high inhibition effectiveness, with respective efficiency, r/, of （90.46±1.30）%, （88.41＋2.24）% and （84.87±4.74）%, in the NaC1 medium. These exhibit good agreements within replicates and statistical methods for the samples. Also, optimal inhibition effectiveness model in the H2SO4 medium was exhibited by 8 g （0.025 4 mol） Na2Cr207 concentration having r/=（78.44±1.10）%. These bear implications for addressing conflicting test data in the study of effective inhibitors for mitigating steel-rebar corrosion in aggressive environments.展开更多
The electrode regulator system is a complex system with many variables, strong coupling and strong nonlinearity, while conventional control methods such as proportional integral derivative （PID） can not meet the req...The electrode regulator system is a complex system with many variables, strong coupling and strong nonlinearity, while conventional control methods such as proportional integral derivative （PID） can not meet the requirements. A robust adaptive neural network controller （RANNC） for electrode regulator system was proposed. Artificial neural networks were established to learn the system dynamics. The nonlinear control law was derived directly based on an input-output approximating method via the Taylor expansion, which avoids complex control development and intensive computation. The stability of the closed-loop system was established by the Lyapunov method. The current fluctuation relative percentage is less than ±8% and heating rate is up to 6.32 ℃/min when the proposed controller is used. The experiment results show that the proposed control scheme is better than inverse neural network controller （INNC） and PID controller （PIDC）.展开更多
文摘Corrosion test data were measured using non-destructive electrochemical techniques and analysed for studying inhibition effectiveness by different concentrations of NazCr207 on the corrosion of concrete steel-rehar in NaC1 and in H2SO4 media. For these, specifications of ASTM G16-95 R04 were combined with the normal and the Gumbel probability density functions as model analytical methods for addressing issues of conflicting reports of inhibitor effectiveness that had generated concerns. Results show that reinforced concrete samples admixed with concentrations having 4 g （0.012 7 tool）, 8 g （0.025 4 mol） and 6 g （0.019 l tool） NaaCr207 exhibited, in that order, high inhibition effectiveness, with respective efficiency, r/, of （90.46±1.30）%, （88.41＋2.24）% and （84.87±4.74）%, in the NaC1 medium. These exhibit good agreements within replicates and statistical methods for the samples. Also, optimal inhibition effectiveness model in the H2SO4 medium was exhibited by 8 g （0.025 4 mol） Na2Cr207 concentration having r/=（78.44±1.10）%. These bear implications for addressing conflicting test data in the study of effective inhibitors for mitigating steel-rebar corrosion in aggressive environments.
基金Project(N100604002) supported by the Fundamental Research Funds for Central Universities of ChinaProject(61074074) supported by the National Natural Science Foundation of China
文摘The electrode regulator system is a complex system with many variables, strong coupling and strong nonlinearity, while conventional control methods such as proportional integral derivative （PID） can not meet the requirements. A robust adaptive neural network controller （RANNC） for electrode regulator system was proposed. Artificial neural networks were established to learn the system dynamics. The nonlinear control law was derived directly based on an input-output approximating method via the Taylor expansion, which avoids complex control development and intensive computation. The stability of the closed-loop system was established by the Lyapunov method. The current fluctuation relative percentage is less than ±8% and heating rate is up to 6.32 ℃/min when the proposed controller is used. The experiment results show that the proposed control scheme is better than inverse neural network controller （INNC） and PID controller （PIDC）.
