ENBIS-17 in Naples

9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017

Harvest Time Prediction for Batch Processes

13 September 2017, 10:50 – 11:10

Abstract

Submitted by
Max Spooner
Authors
Max Spooner (Technical University of Denmark), Murat Kulahci (Technical University of Denmark)
Abstract
In many batch processes there is great variation in batch duration. Some batches progress faster than others due to variation in raw materials, physical conditions and operator behaviour. Product quality is affected by batch harvest time - a batch which is allowed to progress too long will differ to a batch that is harvested prematurely. Often the decision when to harvest depends on the judgement of an expert and requires immediate action. In this work we present a method for predicting the optimal harvest time at an early stage in the ongoing batch based on the data accumulated up to the present. The method is demonstrated using real data. Predictions are updated and gain accuracy as the batch progresses and additional data is accumulated. Besides the process measurements, the batch dynamics (acceleration/deceleration in progress) are incorporated through dynamic time warping. Different approaches to dealing with the high dimensions of batch data, such as variable selection methods and latent structure methods are contrasted. The proposed model allows the harvest time to be predicted at an early stage in the ongoing batch so that preparations can be made downstream and resources may be used accordingly.

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