Modeling of Electrical Energy in Industrial Wastewater Treatment Plant with Traditional and Artificial Neural Network Approaches | ||
| Journal of Hydraulic and Water Engineering | ||
| دوره 2، شماره 1، مهر 2024، صفحه 112-125 اصل مقاله (411.48 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22044/jhwe.2024.15328.1044 | ||
| نویسندگان | ||
| Sahar Saghafi1؛ Tahere Taghizade Firozjaee* 2؛ Fatemeh Kardel3؛ Sina Khoshnevisan4 | ||
| 1Assistant Professor, Faculty of Marine and Oceanic Sciences, University of Mazandaran, Mazandaran, Iran. | ||
| 2Assistant Professor, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran. | ||
| 3Associate Professor, Faculty of Marine and Oceanic Sciences, University of Mazandaran, Mazandaran, Iran. | ||
| 4MSc. Student, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran. | ||
| چکیده | ||
| The rapid development of industries and the establishment of plenty of industrial parks have initiated several environmental issues during recent decades. The environmental standards and rules issued by the environmental organization for increasing the quality of the treated wastewater on the one hand and increasing the energy price on the other hand, have caused the energy management debate to be of particular importance. The main aim of energy management is to minimize the high energy consumption in industrial wastewater treatment plants (IWTP). In this paper, the electric power consumption of IWTP in Amol’s industrial park was measured by implementing both traditional and advanced methods (using artificial neural networks). In the first step, total energy consumption, involving energy used by flow or aeration pumps and mixers was calculated through an energy activity diagram, mathematical equations, and mass balances. In addition, linear regression equations for electrical energy consumption were estimated based on the amount of oxygen needed with an appropriate correlation coefficient. In the next step, a three-layer artificial neural network (ANN) with the Leonberg-Marquard training algorithm was employed. Various parameters, including COD, BOD, total phosphorus, total nitrogen, mixed liquor suspended solids (MLSS), and the flow rate (Q) were employed in 4 models to predict the electrical energy consumption of IWTP. Results showed that COD, MLSS, and Q can be considered as the most important selective indices for the determination of energy consumption by which the highest correlation coefficient and the lowest error rate of 0.928 and 0.0098 were obtained, respectively. | ||
| کلیدواژهها | ||
| Industrial wastewater treatment؛ Electrical energy consumption؛ Three-layer artificial neural network؛ Energy management | ||
| مراجع | ||
|
Abbasi, A. A., & Habibi, M. (2008). Laboratory study of the effect of intake angle on the control of sediment entering the intake in the presence of submerged plates. In Proceedings of the 8th International Seminar on River Engineering. Shahid Chamran University.
Abdel Azim, M., & Fahmy Salah, A. (2010). Physical modeling: Investigation of performance of sediment vanes at intakes. Hydraulics Research Institute, Delta Barrages.
Abul Qasimi, M. (2015). Laboratory investigation of the control of sediment entering the lateral catchment in meander [Doctoral dissertation, Tarbiat Modares University].
Ali Abdoli, M., Falah Nezhad, M., Salehi Sede, R., & Behboudian, S. (2012). Long-term forecasting of solid waste generation by artificial neural networks. Environmental Progress & Sustainable Energy, 31(4), 628–636.
Aminian, P., Ahmadi, A., & Emamgholizadeh, S. (2019). Experimental investigation on the deviated sediment and flow to sediment bypass tunnels (SBTs) using submerged plates. Journal of Hydraulic Structures, 5(2), 18–31.
Barkdoll, B. D., Hagen, B. L., & Odgaard, J. (1995). Sediment exclusion at hydropower intakes using submerged vanes. In Proceedings of the Waterpower Conference (pp. 368–377).
Barkdoll, B. D., Ettema, R., & Odgaard, J. (1999). Sediment control at lateral diversion: Limits and enhancements to vane use. Journal of Hydraulic Engineering, 125(8), 862–870.
Descoins, N., Deleris, S., Lestienne, R., Trouvé, E., & Maréchal, F. (2012). Energy efficiency in wastewater treatment plants: Optimization of activated sludge process coupled with anaerobic digestion. Energy, 41(1), 153–164.
Emamgholizadeh, S., Borojeni, H. S., & Bina, M. (2005). The flushing of the sediments near the power intakes in the Dez reservoir. WIT Transactions on Ecology and the Environment, 83.
Emamgholizadeh, S., & Torabi, H. (2008). Experimental investigation of the effects of submerged vanes for sediment diversion in the Veis (Ahwaz) Pump Station. Journal of Applied Sciences, 8(13), 2396–2403.
Gohri, S., Ayubzadeh, S. A., Qodsian, M., & Salehi Nishabouri, S. A. A. (2009). Sediment control in water intakes using submersible plates. In Proceedings of the 8th International Seminar on River Engineering. Shahid Chamran University.
Hamed, M. M., Khalafallah, M. G., & Hassanien, E. A. (2004). Prediction of wastewater treatment plant performance using artificial neural networks. Environmental Modelling & Software, 19(10), 919–928.
Hasanpour, F. (2005). Investigating the performance of lateral catchment in the presence of submerged composite plates and sills [Doctoral dissertation, Tarbiat Modares University].
Imam, A. (1996). Investigating the effect of the arrangement of submerged plates on the behavior of rivers [Master’s thesis, University of Tehran].
Michell, F., Ettema, R., & Muste, M. (2006). Case study: Sediment control at water intake for large thermal-power station on a small river. Journal of Hydraulic Engineering, 132(5), 440–449.
Nazari, S., & Shafa’i Bejestan, M. (1998). Laboratory investigation of the influence of the angle of deviation and water table height of catchments in river arches on the amount of sediments [Master’s thesis, Shahid Chamran University of Ahvaz].
Sajdi, M., & Habibi, M. (2002). Laboratory investigation of the use of submerged plates and breakwaters in increasing the water intake efficiency. In Proceedings of the 4th Iran Hydraulic Conference. Shiraz University.
Sajdi, M., & Habibi, M. (2007). The use of submerged plates in preventing sediment from entering aquifers. In Proceedings of the Third National Conference on Erosion and Sedimentation.
Singh, K. P., Basant, A., Malik, A., & Jain, G. (2009). Artificial neural network modeling of the river water quality: A case study. Ecological Modelling, 220(6), 888–895.
Singh, P., Carliell-Marquet, C., & Kansal, A. (2012). Energy pattern analysis of a wastewater treatment plant. Applied Water Science, 2, 221–226.
Wang, J.-H., Zhao, X.-L., Guo, Z.-W., Yan, P., Gao, X., Shen, Y., & Chen, Y.-P. (2022). A full-view management method based on artificial neural networks for energy and material-savings in wastewater treatment plants. Environmental Research, 211, 113054.
Wang, Y., Odgaard, J., Melville, B. W., & Jain, S. C. (1996). Sediment control at water intakes. Journal of Hydraulic Engineering, 122(6), 353–356.
Yonesi, H., Omid, M., & Sajdi Ex, M. (2008). The effect of the longitudinal arrangement of submerged plates on the change of riverbed morphology in the vicinity of gravity catchments. In Proceedings of the 6th International Seminar on River Engineering. Shahid Chamran University of Ahvaz. | ||
|
آمار تعداد مشاهده مقاله: 110 تعداد دریافت فایل اصل مقاله: 34 |
||