Identification of optimal measurement points for energy monitoring of industrial processes: The case of milk powder production

Abstract

Meaningful energy analysis of industrial processes requires the installation of energy monitoring systems. However, a lack of systematic methods for identifying the required measurement points, joint to scarce information on the related benefits, results in reluctance from companies in investing in such systems.The paper presents a method for identifying necessary measurement points in industrial processes. It is applied on four milk powder production plants. It combines Pinch Analysis with uncertainty analysis (Monte Carlo simulations), global sensitivity analysis (standardised regression coefficients) and optimisation procedures to solve the “Factor Fixing” and “Variance Cutting” problems. In this way it identifies a limited number of parameters whose precise and accurate measurement is paramount to meaningfully characterise the plant from an energy perspective.Comparing the results obtained from the four case studies it was possible to infer some general traits of milk powder production processes. In particular, 15 out of 60–66 parameters were identified as generally important, their position in the plant was highlighted and the minimum accuracy level required for their measurement was estimated. Such information could subsequently be used for designing an energy monitoring system and giving proof of its benefits to the involved company, by quantifying them in terms of uncertainty reduction in the outcome of the energy analysis.