Modelling the electrical energy profile of a batch manufacturing pharmaceutical facility

Abstract

Sustainable manufacturing practices are a dominating consideration for legacy factories. Major attention is being applied to improving current practices to more sustainable ones. This research provides a case study of a batch manufacturing pharmaceutical facility and compares a number of approaches to modelling the electrical energy consumption in the plant. An accurate model of the electrical energy in the facility will allow more sustainable approaches to be developed. This can be achieved by improving current processes to reduce the electrical load. Historical electrical energy data were modelled using traditional time series methods. Historical manufacturing data and the electrical energy data were used to develop machine learning models using a feedforward neural network and a random forest. All of the approaches were then compared. The major challenge posed in model development and validation was acquiring data suitable for machine learning. The manufacturing data were stored in hand-written records. These records needed to be digitised and then go through a number of transformative steps before the data were suitable for modelling. The random forest model successfully modelled the energy profile of the facility. The model can be used to predict and better manage the plant electrical energy load.