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

JOURNAL OF CLEANER PRODUCTION

卷:284

Article

Bergamini, Riccardo¹  Tuong-Van Nguyen²  Bellemo, Lorenzo³  Elmegaard, Brian¹ 

[1] Tech Univ Denmark, Sect Thermal Energy, Dept Mech Engn, Bldg 403,Nils Koppels Alle, DK-2800 Lyngby, Denmark

[2] Ecole Polytech Fed Lausanne, Ind Proc & Energy Syst Engn Grp, Rue Ind 17, CH-1951 Sion, Switzerland

[3] GEA Proc Engn AS, Gladsaxevej 305, DK-2860 Soborg, Denmark

关键词: Data acquisition;    Energy monitoring;    Industrial processes;    Process integration;    RDRA method;    Retrofit;   

DOI  :  10.1016/j.jclepro.2020.124634

来源: Elsevier

【 摘 要 】

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. (C) 2020 Published by Elsevier Ltd.

【 授权许可】

Free