On-line multivariate statistical monitoring of a fed-batch sugar crystallisation process (original) (raw)
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On-line monitoring of a sugar crystallization process
Computers & Chemical Engineering, 2005
The present paper reports a comparative evaluation of four multivariate statistical process control (SPC) techniques for the on-line monitoring of an industrial sugar crystallization process. The process itself is challenging since it is carried out in multiple phases and there exists strong non-linear and dynamic effects between the variables. The methods investigated include classical on-line univariate statistical process control, batch dynamic principal component analysis (BDPCA), moving window principal component analysis (MWPCA), batch observation level analysis (BOL) and time-varying state space modelling (TVSS). The study is focused on issues of on-line detection of changes in crystallization process operation, the early warning of process malfunctions and potential production failures; problems that have not been directly addressed by existing statistical monitoring schemes. The results obtained demonstrate the superior performance of the TVSS approach to successfully detect abnormal events and periods of bad operation early enough to allow bad batches and related losses in amounts of recycled sucrose to be significantly reduced.
A time varying state space approach for sugar crystallization process modelling and monitoring
Proceedings of the 16th IFAC World Congress, 2005, 2005
The present paper contributes to the issues of batch process modelling and monitoring by proposing a time-varying state space (TVSS) model for the evaporative sugar crystallization industrial process. The study is focused on issues of on-line detection of changes in crystallization process operation, the early warning of process malfunctions and potential production failures; problems that have not been directly addressed by existing statistical monitoring schemes. The TVSS methodology is compared with current state-of-the-art techniques and the results obtained demonstrate the superior performance of the TVSS approach to successfully detect abnormal events and periods of bad operation. Copyright© 2005 IFAC
Chemical Engineering Research and Design, 2009
A large number of production processes for the manufacturing of specialty chemicals, pharmaceuticals, foodstuff, and materials for microelectronics are run in batch mode. Batch processes are "simple" in terms of equipment and operation design, but are often quite complicated in terms of product quality monitoring and of production scheduling and organization. In this paper an industrial case study is presented where the challenges related to the real-time estimation of the required time to manufacture a resin and to the instantaneous product quality estimation are addressed using multivariate statistical techniques. The industrial process is poorly automated, subject to several disturbances, and the batches have uneven lengths. It is shown that stage and batch lengths can be estimated in real time with an average error that is not larger than 20% of the inherent batch-to-batch variability, whereas quality estimations can be provided within the accuracy of the hardware instrumentation, but 240 times faster. The industrial benefits deriving from the use of the proposed monitoring system have been a drastic reduction of the number of samples that need to be analyzed by the lab, prompter adjustment of the processing recipe with consequent reduction of the total processing time, and improved capability to plan the production.
Statistical Process Monitoring of Industrial Batch Processes
2004
The manufacture of high-value products involves many different batch processes, for example industrial fermenters. Such processes require high levels of consistency in their operation to ensure minimal losses of raw materials, utilities and product. Recent application studies have indicated that multivariate statistical technology can provide some support when trying to maintain consistent operation in complex batch processes. This paper summarizes the findings from three case studies involving the application of multivariate statistics to batch processes. Two of the studies are taken from the fermentation industry with the third study involving a comprehensive penicillin production simulation model. The main focus of this paper is to demonstrate how realistic upsets in process operation can be detected and graphically presented using statistical process monitoring technologies. Further to this the paper provides a comparison of different approaches to monitoring batch process operations.
A Regression Model for Estimating Sugar Crystal Size in a Fed-Batch Vacuum Evaporative Crystalliser
2019
Crystallisation occurs in a large group of biotechnological, food, pharmaceutical and chemical processes. These processes are usually carried out in a batch or fed-batch mode. Traditionally, in sugar industry, the crystals quality is examined at the end of the process. Consequently, lack of real time measurement of sugar crystal size in a fed-batch vacuum evaporative crystalliser hinders the feedback control and optimisation of the crystallisation process. A mathematical model can be used for online estimation of the sugar crystal size. Unfortunately, the existing sugar crystallisation models are not in the form suitable for online implementation. Therefore, based on these existing models and seven process variables namely temperature (T), vacuum pressure (Pvac), feed flowrate (Ff), steam flowrate (Fs), crystallisation time (t), initial super-saturation (S0) and initial crystal size (L0), 128 data sets which were obtained from a 2-level factorial experimental design using MINITAB 14...
Journal of pharmaceutical sciences, 2018
The present work presents an in-depth evaluation of continuously collected data during a twin-screw granulation and drying process performed on a continuous manufacturing line. During operation, the continuous line logs 49 univariate process variables, hence generating a large amount of data. Three identical 5-h continuous manufacturing runs were performed. Multivariate data analysis tools, more specifically latent variable modeling tools such as principal component analysis, were used to extract information from the generated data sets unveiling process trends and drifts. Furthermore, a statistical process monitoring strategy is presented. The approach is based on the application of multivariate statistical process monitoring to model the variables that remain around a steady state.
Application of multivariate statistical process control to batch operations
Computers & Chemical Engineering, 2000
This paper summarises the results of a 2-year study focusing on the development of a condition monitoring system for a fed-batch fermentation system operated by Biochemie Ltd. in Austria. Consumer pressure has esulted in a greater emphasis in industry on product quality. As a direct consequence, the importance of accurate process monitoring has increased steadily in recent years. This paper demonstrates the application of multivariate statistical routines to provide process operators with a monitoring tool capable of detecting process abnormalities.
Development of a smart supervisory control system in a sugar mill crystallisation stage
2005
This paper describes the process and proposed technology of using a smart supervisory control system for pan-stage crystallisation operations in raw sugar processing. Intelligent system technologies are to be utilised to provide a standardised approach for pan operations by using key process models and combining them with the collective expertise and knowledge of pan operators. The supervisory control system aims to maximise process throughput and sugar yield while aiming to maintain high sugar quality standards and a steady and low steam usage throughout the process. The system is envisaged to provide a better decision-making strategy for pan-stage operations within the sugar making process, benefiting in reduced costs of sugar manufacture and increased profitability for the Australian sugar industry.