Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.801594
Title: Process energy efficiency and human factors in dairy pasteurisation and other "black box" processes
Author: Challis, Charlotte E.
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2020
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Abstract:
Reduction of C02 emissions is a challenge in all sectors of the process industry. From experience in the food and drink industry we find that process energy reduction and optimisation are often the last areas to be tackled after general good housekeeping, improving efficiency of supply of utilities (hot and chilled water) and investment in new equipment. While this prioritisation has good reason; we find much potential to reduce energy consumption in the running of process plant. We review historical energy studies and highlight that certain processes (pasteurisation, Clean In Place, spray drying) come up regularly as targets for process optimisation due to being high energy consumers and having a degree of complexity. We also find that these processes often lack sufficient monitoring to track energy consumption and define them as “black boxes”. For these “black box” processes we use ethnography to investigate the potential role of the operator to reduce energy consumption. We investigate their autonomy and ability to juggle control priorities; we also look at visualisation issues with regards to the data. If operators are tasked with reducing energy consumption, and provided with data on the energy use of the process; they have the skills and autonomy to achieve that task. Data from operating plant has been extracted and studied to identify energy consumption issues that have gone unnoticed. These are quantified and discussed with operators. Two methods are developed to allow operators to include energy consumption of the plant as a factor they control for. Simple data visualisation is used to provide live plant energy consumption information to operators. This method is trialed with client sites to produce implementation case studies. Then k-means clustering is adapted to identify cases when changes in pasteuriser temperatures and mass flows warrant further investigation and/or action by the operator.
Supervisor: Tierney, Michael ; Wilson, Eddie Sponsor: Not available
Qualification Name: Thesis (Eng.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.801594  DOI: Not available
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