Gate-to-Gate Energy Consumption in Chemical Batch Plants: Statistical Models Based on Reaction Synthesis Type
Artikel i vetenskaplig tidskrift, 2018
Energy consumption in the chemical industry is an important operating cost and environmental impact factor and reducing it is also explicitly mentioned as one of the key principles of green chemistry. Energy consumption has thus been included in diverse process design and evaluation tools as a key metric. However, measurements of energy consumption at the process equipment level are scarce, especially in fine chemical production typically performed in multiproduct and multipurpose batch plants. In this work, we present a shortcut approach based on statistical models, such as probability density functions (PDF) and classification trees, for estimating steam consumption which typically represents the highest energy utility consumption in batch plants. The output of these models is in the form of intervals derived from PDF interquartile ranges and as classes derived from the classification trees, respectively. The validation results (i.e., goodness of fit, cross validation, and case studies) show that the models provide satisfactory interval estimations of steam consumption for benchmarking chemical reaction types and performing uncertainty analysis. The models can be primarily used at early design stages for screening purposes, the reaction type being the minimum needed input information, allowing in the case of classification trees also an analysis of the most influencing predictor variables (i.e., reaction type and operating parameters) upon the steam consumption. This study also demonstrates the use of the PDF statistical models to a previously published case study for the production of the intermediate substance 4-(2-methoxyethyl)-phenol, which can be produced from seven different synthesis routes. The ranking of the synthesis routes according to the PDF models shows similar trends to that of an Energy Loss Index proxy indicator which however requires more detailed chemical and process information.
Life cycle energy inventories
Early design phase metrics
Energy consumption benchmarking