Cstr continuous stirred - tank reactor 連續(xù)攪動水箱式反應(yīng)堆
Dynamics analysis of 3 - variable onlinear chemical reaction in cstr system 化學(xué)反應(yīng)的復(fù)雜動力學(xué)行為分析
The feasibility of the approach is tested in the control of cstr 這種方法的有效性在cstr的控制中得到了驗證。
Modulation induced nonlinear phase transitions of small amplitude oscillation in cstr - bz reaction 反應(yīng)體系小振蕩誘導(dǎo)非平衡相變
4 . the model built in this work is compared with cstr model and pfr model 對本文所建模型與常用的cstr模型和pfr模型作了比較。
Simulation results for a non - isothermal cstr process demonstrate the effectiveness and efficiency of the proposed method 通過非絕熱連續(xù)反應(yīng)釜的仿真驗證了本方法的有效性。
The simulation result of a continuous stirred tank reactor ( cstr ) shows that the data would deviate from normal distribution under parametric uncertainty , and different parameters have distinct effect on data distribution at the same uncertain degree 連續(xù)攪拌釜的仿真結(jié)果表明,在參數(shù)不確定的情況下,數(shù)據(jù)會偏離正態(tài)分布,且在相同的不確定條件下,不同的參數(shù)對最終數(shù)據(jù)的分布影響也不同。
A novel model for residual fluid catalytic cracking process ( rfcc ) is proposed . it divides the whole reactor into two part : the riser as ideal pipe flow reactor and the sett - ler as ideal cstr . the model contains six lumps reaction kinetics with serial and parallel network 通過將實際裝置中發(fā)生裂化反應(yīng)的提升管和沉降段反應(yīng)器分別考慮為理想的活塞流反應(yīng)器和連續(xù)攪拌式反應(yīng)器,建立了簡化的渣油催化裂化反應(yīng)6集總組分的串行和并行反應(yīng)動力學(xué)網(wǎng)絡(luò)模型。
In application , the problems on how to uniquely determine the kinematics inverse solution and how to constitute and simplify the optimization model using iga are mainly considered for redundant manipulator trajectory planning , in the meanwhile , the problem of realtime optimizing the control paramatres using iga in cstr tracking control is also investigated 在實際工程應(yīng)用中,針對冗余機械手軌跡規(guī)劃,主要研究如何唯一確定運動學(xué)逆解以及如何建立和簡化iga的優(yōu)化模型;針對cstr系統(tǒng),主要研究跟蹤控制中利用iga實時優(yōu)化控制參量的問題。
Abstract : an integrating model combining the artificial neura l network with the linear arx model and its identification method is proposed . based on that model , a multivariable nonlinear predictive control algorithm is persented . the algorithm employs the result of the linear predictive control , obtains explicit nonlinear optimal controlling inputs and doesn " t need on - line numerical optimizing which is necessary in general nonlinear model ( including ann model ) predictive control . that greatly decreases on - line computing consumption , strengthens the reliability of the algorithm and the stability of the system . the simulation results in cstr are shown 文摘:提出了一種由人工神經(jīng)網(wǎng)絡(luò)與線性arx模型相結(jié)合的集成模型,給出了其辨識訓(xùn)練方法.以此模型為基礎(chǔ),提出了一種多變量非線性預(yù)測控制算法.它利用線性預(yù)測控制的成果,得到一解析式的非線性優(yōu)化控制輸入,避免了通常非線性模型(包括普通人工神經(jīng)網(wǎng)絡(luò)模型)預(yù)測控制所需的在線數(shù)值尋優(yōu)計算,節(jié)約了在線計算時間,提高了算法的可靠性和穩(wěn)定性.進一步給出了在cstr反應(yīng)器上的仿真實驗結(jié)果