Abstract
Process manufacturing industries constantly strive to make their processes increasingly sustainable from an environmental and economic perspective. A manufacturing system model is a powerful tool to holistically evaluate various manufacturing configurations to determine the most sustainable one. Previously models of process manufacturing systems are typically single target models, trained to fit and/or predict data for a single output variable. However, process manufacturing systems produce a variety of outputs with multiple, sometimes contradictory, sustainability implications. These systems require multiple target models to find the most sustainable manufacturing configuration which considers all outputs. A novel bioprocess that treats process wastewaters to reduce pollutant load for reuse, while simultaneously generating energy in the form of biogas was studied. Multiple target models were developed to predict the percentage removal of chemical oxygen demand and total suspended solids, in addition to the biogas (as volume of methane) produced. Predictions from the models were able to reduce wastewater treatment costs by 17.0%. Eight models were developed and statistically evaluated by the coefficient of determination (R2), normalised root mean square error (nRMSE) and mean absolute percentage error (MAPE). An artificial neural network model built following the ensemble of regressor chains demonstrated the best multi target model performance, averaged across all the bioprocess’s outputs (R2 of 0.99, nRMSE of 0.02, MAPE of 1.74). The model is able to react to new regulations and legislation and/or variations in company, sector, world circumstances to provide the most up to date sustainable manufacturing configuration.
Highlights
Enhancing the sustainability of process manufacturing systems has gained growing attention over the last few decades (Te Liew et al, 2014; Al-Sharrah et al, 2010; Bakshi and Fiksel, 2003)
This paper investigates how multiple target modelling of a process manufacturing system will provide manufacturers with the tools to holistically evaluate the sustainability of a manufacturing system
The random forest (RF) model is inferior at predicting all of the H2AD outputs especially the %chemical oxygen demand (COD) removal (R2 of 0.63, normalised root mean square error (nRMSE) of 0.06 and mean absolute percentage error (MAPE) of 8.56)
Summary
Enhancing the sustainability of process manufacturing systems has gained growing attention over the last few decades (Te Liew et al, 2014; Al-Sharrah et al, 2010; Bakshi and Fiksel, 2003). For a manufacturing system to be considered truly sustainable it needs to maximise environmental and social benefits, in addition to economic benefits (Stock and Seliger, 2016). This is important for process manufacturing, which have multiple outputs, each with several and sometimes contradictory, sustainability implications. Process manufacturing is the thermal and/or bio/ chemical conversion of resources to products, co-products, by-. Process manufacturing covers a range of industries, including chemicals, food and drink, paint, polymer, pharmaceutical, steel, and fastmoving consumer goods (FMCG). All the process outputs (co-/ products, by-products and wastestream composition/volume) require consideration to achieve sustainable manufacturing. This paper investigates how multiple target modelling of a process manufacturing system will provide manufacturers with the tools to holistically evaluate the sustainability of a manufacturing system.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
