With an example of a distillation tower,an important equipment in chemical industry,this paper gives an investigation into the multivariable intelligent controller using the neural network which includes the model of ...With an example of a distillation tower,an important equipment in chemical industry,this paper gives an investigation into the multivariable intelligent controller using the neural network which includes the model of the Plant,the multivariable control systems,and the model-free intelligent control algorithm.The simulation results show that such a novel controller is effective to control the complex chemical prncesses.Having good performances,strong robustness to the changes of the process model and simple structure,it can be conveniently used in practical engineering.展开更多
The challenges posed by smart manufacturing for the process industries and for process systems engineering（PSE） researchers are discussed in this article. Much progress has been made in achieving plant- and site-wid...The challenges posed by smart manufacturing for the process industries and for process systems engineering（PSE） researchers are discussed in this article. Much progress has been made in achieving plant- and site-wide optimization, hut benchmarking would give greater confidence. Technical challenges confrontingprocess systems engineers in developing enabling tools and techniques are discussed regarding flexibilityand uncertainty, responsiveness and agility, robustness and security, the prediction of mixture propertiesand function, and new modeling and mathematics paradigms. Exploiting intelligence from big data to driveagility will require tackling new challenges, such as how to ensure the consistency and confidentiality ofdata through long and complex supply chains. Modeling challenges also exist, and involve ensuring that allkey aspects are properly modeled, particularly where health, safety, and environmental concerns requireaccurate predictions of small but critical amounts at specific locations. Environmental concerns will requireus to keep a closer track on all molecular species so that they are optimally used to create sustainablesolutions. Disruptive business models may result, particularly from new personalized products, but that isdifficult to predict.展开更多
文摘With an example of a distillation tower,an important equipment in chemical industry,this paper gives an investigation into the multivariable intelligent controller using the neural network which includes the model of the Plant,the multivariable control systems,and the model-free intelligent control algorithm.The simulation results show that such a novel controller is effective to control the complex chemical prncesses.Having good performances,strong robustness to the changes of the process model and simple structure,it can be conveniently used in practical engineering.
文摘The challenges posed by smart manufacturing for the process industries and for process systems engineering（PSE） researchers are discussed in this article. Much progress has been made in achieving plant- and site-wide optimization, hut benchmarking would give greater confidence. Technical challenges confrontingprocess systems engineers in developing enabling tools and techniques are discussed regarding flexibilityand uncertainty, responsiveness and agility, robustness and security, the prediction of mixture propertiesand function, and new modeling and mathematics paradigms. Exploiting intelligence from big data to driveagility will require tackling new challenges, such as how to ensure the consistency and confidentiality ofdata through long and complex supply chains. Modeling challenges also exist, and involve ensuring that allkey aspects are properly modeled, particularly where health, safety, and environmental concerns requireaccurate predictions of small but critical amounts at specific locations. Environmental concerns will requireus to keep a closer track on all molecular species so that they are optimally used to create sustainablesolutions. Disruptive business models may result, particularly from new personalized products, but that isdifficult to predict.
