A hybrid method for synthesis of integrated water and regeneration networks with variable removal ratios.
Abstract This work presents a systematic optimization framework that integrates graphical insights with mathematical modelling to reduce both model complexity and computational time. The graphical technique adopted in this work is Composite Table Algorithm (CTA), which is improved to determine an op...
| Publicado en: | Journal of Environmental Management Vol. 231; pp. 666 - 679 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Academic Press Inc.
Feb2019
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | Abstract This work presents a systematic optimization framework that integrates graphical insights with mathematical modelling to reduce both model complexity and computational time. The graphical technique adopted in this work is Composite Table Algorithm (CTA), which is improved to determine an optimal regenerator removal ratio (RR) that simultaneously minimizes the freshwater requirement and wastewater generation within the water network. The improved CTA is demonstrated using literature examples for both fixed load and fixed flowrate problems. It is further adapted to solve a multiple contaminant problem using the reference contaminant approach. The mathematical model developed includes a detailed design of a reverse osmosis (RO) unit to allow for simultaneous optimization of water and energy used by the regenerator. This provides accurate cost estimation of the water network rather than the linear cost functions associated with the use of blackbox representations in graphical targeting. Upon integrating the graphical and mathematical techniques in this study, results show that there was a reduction of about 85% in CPU time. This implies that the model converges faster and therefore favours the use of insight-based techniques as a preprocessing step for mathematical modelling. Highlights • A hybrid method that combines graphical and mathematical approaches. • The method uses the graphical technique as a preprocessing step that improves the detailed mathematical model. • Noticeable decrease in CPU time. • The method is very promising for large scale problems. • The method takes advantage of both graphical and mathematical methods. |
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