| 1. | Cstr continuous stirred - tank reactor
连续搅动水箱式反应堆 |
| 2. | Dynamics analysis of 3 - variable onlinear chemical reaction in cstr system
化学反应的复杂动力学行为分析 |
| 3. | The feasibility of the approach is tested in the control of cstr
这种方法的有效性在cstr的控制中得到了验证。 |
| 4. | Modulation induced nonlinear phase transitions of small amplitude oscillation in cstr - bz reaction
反应体系小振荡诱导非平衡相变 |
| 5. | 4 . the model built in this work is compared with cstr model and pfr model
对本文所建模型与常用的cstr模型和pfr模型作了比较。 |
| 6. | Simulation results for a non - isothermal cstr process demonstrate the effectiveness and efficiency of the proposed method
通过非绝热连续反应釜的仿真验证了本方法的有效性。 |
| 7. | 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
连续搅拌釜的仿真结果表明，在参数不确定的情况下，数据会偏离正态分布，且在相同的不确定条件下，不同的参数对最终数据的分布影响也不同。 |
| 8. | 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
通过将实际装置中发生裂化反应的提升管和沉降段反应器分别考虑为理想的活塞流反应器和连续搅拌式反应器，建立了简化的渣油催化裂化反应6集总组分的串行和并行反应动力学网络模型。 |
| 9. | 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
在实际工程应用中，针对冗余机械手轨迹规划，主要研究如何唯一确定运动学逆解以及如何建立和简化iga的优化模型；针对cstr系统，主要研究跟踪控制中利用iga实时优化控制参量的问题。 |
| 10. | 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:提出了一种由人工神经网络与线性arx模型相结合的集成模型，给出了其辨识训练方法.以此模型为基础，提出了一种多变量非线性预测控制算法.它利用线性预测控制的成果，得到一解析式的非线性优化控制输入，避免了通常非线性模型(包括普通人工神经网络模型)预测控制所需的在线数值寻优计算，节约了在线计算时间，提高了算法的可靠性和稳定性.进一步给出了在cstr反应器上的仿真实验结果 |
