No abstract

No abstract

No abstract

The notion of Internet of Things (IoT), as well as related topics such as Cyber-Physical Systems, Industrie 4.0 and Smart Manufacturing are currently attracting a lot of attention within the process and manufacturing industries. Clearly, IoT offers many potential applications for automation, ranging from engineering installation of new plants to production management and more intelligent maintenance schemes including novel sensor technologies. The focus of this paper is, however, on the control and operations. Most likely IoT leads to new system architectures where open standards play a significant role. Through better connectivity, information will be much more easily available, which could result in that previously isolated functions will become more closely integrated.Here modeling at the right level of fidelity will be absolutely key. It can be expected that the importance of optimization will increase and this paper discusses some aspects related to the opportunities, challenges and changes triggered by IoT.

No abstract

No abstract

No abstract

Performing experiments for system identification is often a time-consuming task which may also interfere with the process operation. With memory prices going down and the possibility of cloud storage, years of data is more and more commonly stored (without compression) in a history database. In such stored data, there may already be intervals informative enough for system identification. Therefore, the goal of this project was to find an algorithm that searches and marks intervals suitable for process identification (rather than completely autonomous system identification). For each loop, four stored variables are required: setpoint, manipulated variable, measured process output and mode of the controller. The essential features of the method are the search for excitation of the input and output, followed by the estimation of a Laguerre model combined with a hypothesis test to check that there is a causal relationship between process input and output. The use of Laguerre models is crucial to handle processes with deadtime without explicit delay estimation. The method was tested on three years of data from about 200 control loops. It was able to find all intervals in which known identification experiments were performed as well as many other useful intervals in closed/open loop operation.