Prediction model for electric power consumption of seawater desalination based on machine learning by seawater quality change in future

Kyudae Shim; Young-Hee Ko

doi:10.3741/JKWRA.2021.54.S-1.1023

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2021 Vol.54, Issue 12S Next Page

Research Article

Prediction model for electric power consumption of seawater desalination based on machine learning by seawater quality change in future 장래 해수수질 변화에 따른 머신러닝 기반 해수담수 전력비 예측 모형 개발

31 December 2021. pp. 1023-1035

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Abstract

The electricity cost of a desalination facility was also predicted and reviewed, which allowed the proposed model to be incorporated into the future design of such facilities. Input data from 2003 to 2014 of the Korea Hydrographic and Oceanographic Agency (KHOA) were used, and the structure of the model was determined using the trial and error method to analyze as well as hyperparameters such as salinity and seawater temperature. The future seawater quality was estimated by optimizing the prediction model based on machine learning. Results indicated that the seawater temperature would be similar to the existing pattern, and salinity showed a gradual decrease in the maximum value from the past measurement data. Therefore, it was reviewed that the electricity cost for seawater desalination decreased by approximately 0.80% and a process configuration was determined to be necessary. This study aimed at establishing a machine-learning-based prediction model to predict future water quality changes, reviewed the impact on the scale of seawater desalination facilities, and suggested alternatives.

Keywords

Machine learning

Seawater temperature

Salinity

Prediction model

Electric power

본 연구는 머신러닝 기반의 분석으로 해수담수화(Desalination) 시설의 전력비 예측모델의 가능성을 검토하였다. 해수담수화 주요 공정인 역삼투(Seawater Reverse Osmosis) 시설의 전력비 예측 모델을 개발하고, 전력비 산정에 영향을 미치는 인자를 분석하였으며, 해수 수질 중에서 선정된 수온 및 염분도 측정자료를 활용하여 검토하였다. 국립해양조사원(Korea Hydrographic and Oceanographic Agency, KHOA)의 2003년부터 2014년까지의 자료를 이용하였으며, 모형의 구조는 시행오차법(Trial & Error)으로 하이퍼파라미터를 최적화하여 머신러닝 기반의 예측 모델을 구축하고, 장래 해수 수질을 예측하였다. 해수 수온은 기존 패턴과 유사할 것으로 예측되었고, 염분도는 과거 측정자료 범위 이내로 최대값이 점차 감소되는 경향을 보여 해수담수화의 전력비가 약 0.80% 감소하는 것으로 검토되었다. 본 연구는 머신러닝 기반의 예측 모델을 구축하여 장래 수질 변화 예측하였으며, 해수 수질 변동의 영향 및 대안을 제시했다는데 의의가 있다.

키워드

머신러닝

해수 수온

염분도

예측 모델

전력비

References

Badruzzaman, M., Voutchkov, N., Weinrich, L., and Jacangelo, J.G. (2019). "Selection of pretreatment technologies for seawater reverse osmosis plants: A review." Desalination, Vol. 449, pp. 78-91. 10.1016/j.desal.2018.10.006

Bernat, X., Gibert, O., Guiu, R., Tobella, J., and Campos C. (2010). The economics of desalination for various uses. Re-thinking Water and Food Security: Fourth Botín Foundation Water Workshop, CRC Press, London, pp. 329-346.

Cho, H., Kim, D., Olivera, F., and Guikema, S.D. (2011). "Enhanced speciation in particles swarm optimization for multi-modal problems." European Journal of Operational Research, Vol. 213, No. 1, pp. 15-23. 10.1016/j.ejor.2011.02.026

Choi, H.M., Kim, M.K., and Yang, H. (2020). "High water temperature prediction using RNN-based LSTM." Proceedings of the Korea Institute of Information and Communication on Conference, KIISE, pp. 693-695.

Duda, R.O., Hart, P.E., and Stork, D.G. (2001). Pattern classification, Wiley-Interscience, New York, NY, U.S., pp. 3-11.

Fritzmann, C., Löwenberg, J., Wintgens, T., and Melin, T. (2007). "State of-the-art of reverse osmosis desalination." Desalination, Vol. 216, pp. 1-76. 10.1016/j.desal.2006.12.009

Global Water Intelligence (GWI) (2015). Desalination markets 2016. Oxford, UK., p. 93.

Greenlee, L.F., Lawler, D.F., Freeman, B.D., Marrot, B., and Moulin, P. (2009). "Reverse osmosis desalination: Water sources, technology, and today's challenges." Water Research, Vol. 43, No. 9, pp. 2317-2348. 10.1016/j.watres.2009.03.01019371922

Jeon, S.B, Oh, H.Y., and Jeong, M.H. (2020). "Estimation of sea water quality level using machine learning." Journal of Korean Society for Geospatial Information Science, Vol. 28, No. 4, pp. 145-152. 10.7319/kogsis.2020.28.4.145

Jeong, S., and Park, S. (2020). "Prediction of water temperature change using LSTM-based deep learning technology." Proceedings of the Korea Institute of Information and Communication on Women's ICT Conference, KIICE, pp. 42-43.

Jun, G.I., Kwon, D.H., and Ki, S.J. (2020). "Comparing the performance of machine learning algorithms in predicting river water quality and quantity." KSWST Journal of Water Treatment, Vol. 28, No. 1, pp. 49-57. 10.17640/KSWST.2020.28.1.49

Jung, S., Kim, Y.J., Park, S., and Im, J.H. (2020). "Prediction of sea surface temperature and detection of ocean heat wave in the South Sea of Korea using time-series deep-learning approaches." Korean Journal of Remote Sensing, Vol. 36, No. 5-3, pp. 1077-1093.

Kim, G.J., and Youm, K.H. (2019). "Design of dead-end membrane module with increased permeate flux by natural convection instability flow." Korean Membrane Journal, Vol. 29, No. 3, pp. 147-154. 10.14579/MEMBRANE_JOURNAL.2019.29.3.147

Kim, I.S., and Oh, B.S. (2008). "Technologies of seawater desalination and wastewater reuse for soving water shortage." Korean Society of Environmental Engineers, Vol. 30, No. 12, pp. 1197-1202.

Kim, Y.H., Im, J., Ha, H.K., Choi, J.K. and Ha, S. (2014). "Machine learning approaches to coastal water quality monitoring using GOCI satellite data." GIScience & Remote Sensing, Vol. 51, No. 2, pp. 158-174. 10.1080/15481603.2014.900983

Kurihara, M., and Takeuchi, H. (2013). "Mega-ton water system: Japanese national research and development project on seawater desalination and wastewater reclamation." Desalination, Vol. 308, pp. 131-137. 10.1016/j.desal.2012.07.038

K-water (2019). The feasibility and basic plan for SWRO project in Daesan industrial area (in Korean), pp. 6-80, 6-155, 6-179, 6-189, 6-192.

Kwon, B.S., Lee, S.H., Kang, S.T., and Lim, J.L. (2020). "Current research trends and the need for localization in ultrapure water production facilities in semiconductor industries." Korean Society of Environmental Engineers, Vol. 42, No. 10, pp. 493-512. 10.4491/KSEE.2020.42.10.493

Lee, G.O., and Park, J.Y. (2020). "A case study on utilization of industrial wastewater reuse." The Korean Society of Industry Convergence, Vol. 23, No. 1, pp. 17-24.

Lee, S.M., and Kim, I.K. (2021). "A comparative study on the application of boosting algorithm for Chl-a Estimation in the downstream of Nakdong River." Journal of Korean Society of Environmental Engineers, Vol. 43, No. 1, pp. 66-78. 10.4491/KSEE.2021.43.1.66

Mitchell, T.M. (1997). Machine learning. McGraw-Hill Science/Engineering/Math, New York, NY, U.S., pp. 81-108.

Oh, I.S. (2008). Pattern recognition. Kyobo, pp. 1-15, 95-132.

Park, J.S., and Lee, H.H. (2020). "Prediction of high turbidity in rivers using LSTM algorithm." Journal of Korean Society of Water and Wastewater, Vol. 34, No. 1, pp. 35-43. 10.11001/jksww.2020.34.1.035

Rosenblatt, F., Stieber, A., and Shatz, R.H. (1957). The perceptron a perceiving and recongnizing automaton, Report No. 85-460-1, Cornell Aeronautical Laboratory, Inc., New York, NY, U.S., pp. 1-2.

Suh, S.H., Kim, K.W., Kim, H.H., Yoon, I.S., and Cho, M.T. (2015). "Evaluation of energy saving with vector control inverter driving centrifugal pump system." KSFM Journal of Fluid Machinery, Vol. 18, No. 2, pp. 67-72. 10.5293/kfma.2015.18.2.067

Tariq, R. (2016). Make your own neural network, Createspace Independent Publishing Platform, CA, U.S., p. 43.

Voutchkov, N. (2010). Desalination engineering: Planning and design, McGraw-Hill, New York, , NY, U.S., pp. 17, 73, 115.

Voutchkov, N. (2018). "Energy use for membrane seawater desalination - current status and trends." Desalination, Vol. 431, pp. 2-14. 10.1016/j.desal.2017.10.033

Wikipedia (2021). Neuron, accessed 08 August 2021, <https://ko.wikipedia.org/wiki/%EC%8B%A0%EA%B2%BD_%EC%84%B8%ED%8F%AC>.

Woo, S.W., and Kim, Y.H. (2017). "Reduction of power consumption by variable speed operation of high pressure pump in seawater reverse osmosis desalination plant." KSFM Journal of Fluid Machinery, Vol. 20, No. 5, pp. 33-39. 10.5293/kfma.2017.20.5.033

Xiao, C., Chen, N., Hu, C., Wang, K., Xu, Z., Cai, Y., Xu, L., Chen, Z., and Gong, J. (2019). "A spatiotemporal deep learning model for sea surface temperature field prediction using time-series satellite data." Environmental Modelling & Software, Vol. 120, 104502 10.1016/j.envsoft.2019.104502

Yang, Y., Dong, J., Sun, X., Lima, E., Mu, Q., and Wang, X. (2017). "A CFCC-LSTM model for sea surface temperature prediction." IEEE Geoscience and Remote Sensing Letters, Vol. 15, No. 2, pp. 207-211. 10.1109/LGRS.2017.2780843

Zhang, Q., Wang, H., Dong, J., Zhong, G. and Sun, X. (2017). "Prediction of sea surface temperature using long short-term memory." IEEE Geoscience and Remote Sensing Letters, Vol. 14, No. 10, pp. 1745-1749. 10.1109/LGRS.2017.2733548

Information

Publisher :KOREA WATER RESOURECES ASSOCIATION
Publisher(Ko) :한국수자원학회
Journal Title :Journal of Korea Water Resources Association
Journal Title(Ko) :한국수자원학회 논문집
Volume : 54
No :12
Pages :1023-1035
Received Date : 2021-08-23
Revised Date : 2021-09-23
Accepted Date : 2021-09-27
DOI :https://doi.org/10.3741/JKWRA.2021.54.S-1.1023

Journal of Korea Water Resources Association ISSN:2799-8746(Print) 2799-8754(Online) 한국수자원학회 논문집

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