Abstract
In this paper, a data-mining approach for modeling pumps in the wastewater preliminary treatment process is discussed. Data-mining algorithms are utilized to develop pump performance models based on industrial data collected at a municipal wastewater processing plant. The performance of wastewater pumps is described by two parameters, pump energy consumption and water flow rate after the pumps. Two types of models, dynamic and steady state, are established to predict pump energy consumption and water flow rate. The first type of model is developed based on 5-min data and the second type is built based on 30-min data. The accuracy of the models has been validated. © 2011 American Society of Civil Engineers.
| Original language | English |
|---|---|
| Pages (from-to) | 159-168 |
| Journal | Journal of Energy Engineering |
| Volume | 137 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Dec 2011 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 6 Clean Water and Sanitation -
SDG 9 Industry, Innovation, and Infrastructure -
SDG 11 Sustainable Cities and Communities
Research Keywords
- Data mining
- Dynamic models
- Energy consumption
- Head influence
- Neural networks
- Pump energy
- Static models
- Wastewater pump models
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