Data-driven Based Process Monitoring And Optimization

The concept of "data-driven" is of ten used in computer science. Butbecause of the exceeding progress of computer techniques, massive processdata can be obtained by the intelligentized industry, so "data-driven"has been getting more and more attention in engineering science. Rapidimprovement of capacity and speed of database and massive data obtainedby database make it abstractive how to use them more effectively and tofulfill more functions, but not just show them on panels and screens. Datameans information, and information represents object, so the so-called"data-driven" is drawing information from data, and use information torealize different objects. To draw information from data, statisticaltechniques are the chief methods, and application based on multivariatestatistical techniques become the main part of data-driven area. Atpresent, there have been many papers about different application todifferent level of industry based on data-driven algorithm and techniques.If link the different level application on conjunct data, the wholeindustry process could be covered by control system based on data-driven,that is, data-driven factory. Then from the bottom loop control to upperoptimization to system monitoring and diagnosis, all levels can beconnected by data stream forming a unified logical system. This unifiedsystem can make full use of data in "micro" and "macro" levels. Inthis paper, data-driven industry process control system was broughtforward and its concept and development was discussed as well asapplications to concrete levels were studied. At the same time, severalnew idea and methods were put forward and validated on simulations. Thoughnot all problem could be discussed detailedly here, there is a wish thatthis work could be a complement of study field and could offer a fewcommonplace remarks by way of introduction so that others may come up with valuable opinions.Data-driven industry process control system can be separated as three levels: local loop, local process (or batches) and global process. The main innovational work of this paper are as following:1) Introduce the data-driven industry process control system and the system drawing. Give summarization of the study of the whole system and every level. Mainly expatiate on modeling, multivariate statistical process monitoring and diagnosis (that is, multivariate statistical process control, MSPC), operational optimization and quality optimization, controller design as well as control performance monitoring and diagnosis, based on multivariate statistical techniques. The expatiation is about all aspects including theories, algorithm and application.2) Conventional principal component analysis is based on the assumption that data obeys normal school. If data does not satisfy the assumption, PCA loses its effect, but independent component analysis (ICA) can be more efficient. On the other hand, if data is not normal school, the way calculating control limits will change, and non-parameter method kernel density estimation (KDE) will be used instead of parameter method. So PCA, ICA, PCA with KDE and ICA with KDE were brought forward and applied to a real factory process, the results turned to be effective.3) Principal component model is linear static model, and can not deal with dynamic or time-variant system. To overcome this problem, dynamic recursive algorithm was brought forward, and application to two typical chemical processes and a 3-phase symmetrical induction motors shows effect of the method.4) Consecutive process and batch process monitoring and diagnosis have been studied relatively widely, but trade transition monitoring and diagnosis rarely occur. Only if transition process was included in monitoring and diagnosis system, the system could be complete. Because transition data were intercepted sectional from process, the transition process monitoring and diagnosis could be dealt with by way of batch with MPCA and MPLS. On the other hand, transition processes are of ten nonlinear dynamic process, so MPCA and MPLS were altered to dynamic multiway PCA (DMPCA) and dynamic multiway PLS (DMPLS). DMPLS can also monitor the object quality variables. At last DMPCA and DMPLS were applied to a polypropylene set of a petrochemical company, and the results revealed the capability and potential of the method.5) Some advantages of using PLS as part of control system design include automatic decoupling and efficient loop paring, as well as natural handling of nonsquare systems and poorly conditioned systems. But if the latent variable can not be complete independent, the advantages will disappear and tuning of controller will be difficult. So in this paper, a methodology is proposed for control based on optimization in the subspace defined by the latent variable model, Some simulations were applied to two processes to testify the performance of the method and the results turned out to be affirmative.6) Trade transition is the key part of modern chemical industry, which is very important for the factory benefit. At present, trade transition optimization still mainly use mechanical model, or half mechanical and half experiential model, but rarely use experiential model. While partial least square model (PLS) is experiential model which can make full use of a mass of data, and trade transition operation trajectory optimization using PLS model, both PLS and mechanical model, and PLS reverse latent space were presented which could simplify the modeling step and optimization.Finally, the paper concluded the research findings, and pointed out some future research areas.