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Commercially, the Czochralski process plays a key role in production of monocrystalline silicon for semiconductor and solar cell applications. However, it is a highly complex batch process which requires careful control throughout the whole crystal production. In the present paper, an iterative method based on model predictive control (MPC) for calculating a new and improved trajectory from a growth run to the next is explored. The method uses the results of the previous growth run in combination with an underlying model which incorporates the complex dynamic effect of the heater temperature on the pulling rate. The motivation behind this choice of strategy is to enhance the quality of the fully grown ingot from one run to the next by applying the most recent estimates of the unknown parameters. The results show that combining MPC, estimation and Run-To-Run control has enabled simulation of effective control of the Czochralski crystallization process. © 2017. This is the authors’ accepted and refereed manuscript to the article. Locked until 4.5.2019 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

An integrated system for pollution control has been developed and is installed in a fertiliser plant. The aim of the system is to improve the quality control at all levels by streamlining the information flow from the process to operators and management. The system is composed of an operator support system that supplements the distributed control system, and a reporting and documentation system that generates reports at several levels of detail, tailored to the needs of individual users. At several levels of detail, discharge data is compared to limits imposed by the authorities and by company policy. This enables rapid corrective action by operators and management. The system may form the basis of a more general tool for production quality management.

An operator support system for detection of process upsets and determining their cause is presented. An important part of the system is a total plant model where each process unit is represented as an attributed process object and where the process flows are modelled as relations between process objects in the form of process connections. For each process connection, a number of measured or calculated process variables are monitored for deviations. Once a deviation has been detected, the process object connected at the input of the process connection checks all incoming streams to see if the upset may have been caused by an upstream process object. If so, control is passed to the diagnoser associated with the upstream process unit, which in tum may pass control to a unit further upstream. This goes on until a diagnoser finds no relevant deviations in any of its upstream units. The diagnoser then decides that the cause of the upset is internal and an internal diagnosis of the process object is initiated. The diagnostic process can be controlled or overridden by the operator. The operator interaction is based on animated process flowsheet pictures where the diagnostic search is indicated by colour coded process object and process connections. The internal diagnostics of the system is based on quantitative techniques such as process models, fault tree models and Kaiman filtering methods. The topological search is rule based.