Development of a Machine Learning Model for Predicting Hardness in the Water Treatment Pharmaceutical Industry

IF 2.4 Q2 ENGINEERING, INDUSTRIAL

International Journal of Industrial Engineering and Management Pub Date : 2023-06-30 DOI:10.24867/ijiem-2023-2-329

Al Ansor Siahaan, M. Asrol

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引用次数: 0

Abstract

[1] D. Askenaizer and M. Watson Engineers, “Drinking Water Quality and Treatment,” 2001. [2] S. Sulistyani, A. Fillaeli, U. Negeri, and Y. K. Malang, “Uji kesadahan air tanah di daerah sekitar pantai kecamatan rembang propinsi jawa tengah,” 2012. [3] H. Elfil and A. Hannachi, “Reconsidering water scaling tendency assessment,” AIChE Journal, vol. 52, no. 10, pp. 3583–3591, Oct. 2006, doi: 10.1002/aic.10965. [4] A. Sharjeel, S. Anwar, A. Nasir, and H. Rashid, “Design, development and performance of optimum water softener,” Earth Sciences Pakistan, vol. 3, no. 1, pp. 23–28, Jan. 2019, doi: 10.26480/esp.01.2019.23.28. [5] A. Sircar, K. Yadav, K. Rayavarapu, N. Bist, and H. Oza, “Application of machine learning and artificial intelligence in oil and gas industry,” Petroleum Research, vol. 6, no. 4. KeAi Publishing Communications Ltd., pp. 379–391, Dec. 01, 2021. doi: 10.1016/j. ptlrs.2021.05.009. [6] J. Jawad, A. H. Hawari, and S. Zaidi, “Modeling of forward osmosis process using artificial neural networks (ANN) to predict the permeate flux,” Desalination, vol. 484, Jun. 2020, doi: 10.1016/j.desal.2020.114427. [7] S. Singha, S. Pasupuleti, S. S. Singha, R. Singh, and S. Kumar, “Prediction of groundwater quality using efficient machine learning technique,” Chemosphere, vol. 276, Aug. 2021, doi: 10.1016/j.chemosphere.2021.130265. [8] A. Bannoud, “The electrochemical way of removing the hardness of water,” 1993. [9] A. Mahvi, N. Dariush, V. Forugh, and S. Nazmara, “Teawaste as An Adsorbent for Heavy Metal Removal from Industrial Wastewaters,” Am. J. Appl. Sci., vol. 2, Jan. 2005, doi: 10.3844/ajassp.2005.372.375. [10] C. C. Aggarwal, Neural Networks and Deep Learning. Springer International Publishing, 2018. doi: 10.1007/978-3-319-94463-0. [11] P. Goyal, S. Pandey, and K. Jain, “Unfolding Recurrent Neural Networks,” in Deep Learning for Natural Language Processing, Apress, 2018, pp. 119–168. doi: 10.1007/978-1-4842-3685-7_3. [12] Z. Zhao, W. Chen, X. Wu, P. C. Y. Chen, and J. Liu, “LSTM network: A deep learning approach for Short-term traffic forecast,” IET Intelligent Transport Systems, vol. 11, no. 2, pp. 68–75, Mar. 2017, doi: 10.1049/iet-its.2016.0208. [13] A. Saxena and T. R. Sukumar, “Predicting bitcoin price using lstm And Compare its predictability with arima model,” Int. J. Pure Appl. Math., vol. 119, no. 17, pp. 2591–2600, Feb. 2018, doi: 10.13140/RG.2.2.15847.57766. [14] N. K. Manaswi, “RNN and LSTM BT Deep Learning with Applications Using Python : Chatbots and Face, Object, and Speech Recognition With TensorFlow and Keras,” N. K. Manaswi, Ed. Berkeley, CA: Apress, 2018, pp. 115–126, doi: 10.1007/978-14842-3516-4_9. [15] A. Sherstinsky, “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network,” Phys. D Nonlinear Phenom., vol. 404, p. 132306, 2020, doi: 10.1016/j.physd.2019.132306. [16] H. Chung and K. S. Shin, “Genetic algorithm-optimized long short-term memory network for stock market prediction,” Sustainability (Switzerland), vol. 10, no. 10, Oct. 2018, doi: 10.3390/su10103765. [17] R. Couronné, P. Probst, and A. L. Boulesteix, “Random forest versus logistic regression: A large-scale benchmark experiment,” BMC Bioinformatics, vol. 19, no. 1, Jul. 2018, doi: 10.1186/s12859-018-2264-5. [18] L. Breiman, “Random Forests,” 2001. [19] G. Shmueli, P. C. Bruce, I. Yahav, N. R. Patel, and K. C. Lichtendahl Jr., Data mining for business analytics: concepts, techniques, and applications in R. Wiley, 2017. [20] D. A. Lind, W. G. Marchal, and S. A. Wathen, Statistical techniques in business & economics. McGraw-Hill, 2017. References Development of a Machine Learning Model for Predicting Hardness in the Water Treatment Pharmaceutical Industry

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水处理制药行业硬度预测机器学习模型的开发

[10] D. Askenaizer和M. Watson Engineers，“饮用水质量和处理”，2001。[10]李建军，刘建军，刘建军，“空气质量与空气质量的关系”，《中国环境科学》，2012。[10]李建军，“水结垢倾向评价的再思考”，《中国给水排水》，第2期。10, pp. 3583-3591, Oct. 2006, doi: 10.1002/aic.10965。[10]王晓明，王晓明，“水软化剂的设计、开发与性能研究”，《地球科学》第3卷，第2期。2019年1月，第23-28页，doi: 10.26480/esp.01.2019.23.28。[10] A. Sircar, K. Yadav, K. Rayavarapu, N. Bist, H. Oza，“机器学习和人工智能在石油和天然气工业中的应用”，石油研究，第6卷，第6期。4. 科爱出版传播有限公司，379-391页，2021年12月1日。doi: 10.1016 / j。ptlrs.2021.05.009。[10]王晓明，王晓明，王晓明，“基于人工神经网络(ANN)的海水正向渗透模型研究”，《海洋工程学报》，2014年6月，doi: 10.3969 / j.i ssn . 1006 - 1007。[10] S. Singha, S. Pasupuleti, S. S. Singha, R. Singh, S. Kumar，“基于高效机器学习技术的地下水水质预测”，环境科学，vol. 276, Aug. 2021, doi: 10.1016/j.c chemosphere.2021.130265。[10] A. Bannoud，“电化学方法去除水的硬度”，1993。[10]张晓明，张晓明，张晓明，“工业废水中重金属的吸附研究”，环境科学与技术，2011。j:。科学。， vol. 2, 2005年1月，doi: 10.3844/ ajasp .2005.372.375。[10] C. C. Aggarwal，神经网络与深度学习。b施普林格国际出版，2018。doi: 10.1007 / 978-3-319-94463-0。[10] P. Goyal, S. Pandey和K. Jain，“展开递归神经网络”，《自然语言处理的深度学习》，Apress, 2018，第119-168页。doi: 10.1007 / 978 - 1 - 4842 - 3685 - 7 - _3。[10]赵振宇，陈伟，吴晓霞，刘建军，“基于深度学习的LSTM网络短期交通预测方法”，智能交通系统，vol. 11, no. 1。2, pp. 68-75, 2017年3月，doi: 10.1049/ et-its.2016.0208。[10] A. Saxena和T. R. Sukumar，“使用lstm预测比特币价格并将其可预测性与arima模型进行比较”，Int。纯苹果。数学。，第119卷，第119号。17, pp. 2591 - 26,2018, doi: 10.13140/RG.2.2.15847.57766。N. K. Manaswi，“RNN和LSTM BT深度学习与使用Python的应用:Chatbots和Face, Object, and Speech Recognition with TensorFlow and Keras，”N. K. Manaswi, Ed. Berkeley, CA: Apress, 2018, pp. 115-126, doi: 10.1007/978-14842-3516-4_9。[10] A. Sherstinsky，“递归神经网络(RNN)和长短期记忆(LSTM)网络的基础”，物理学报。D非线性现象。中国科学，第404卷，第132306页，2020,doi: 10.1016/j.p yphys.2019.132306。[10]郑宏祥，“基于遗传算法优化的长短期记忆网络在股票市场预测中的应用”，vol. 10, no. 10。2018年10月10日，doi: 10.3390/su10103765。[10]李建军，李建军，“随机森林与逻辑回归的关系:一个大规模的基准实验”，《生物信息学》vol. 19, no. 1。2018年7月1日，doi: 10.1186/s12859-018-2264-5。[18] L. Breiman，《随机森林》，2001。[10]杨建军，杨建军，李建军，基于数据挖掘的商业分析:概念、技术和应用[j] .计算机科学与技术，2017。[10]林志刚，王志刚，王志刚，商业与经济的统计技术。麦格劳-希尔,2017年。水处理制药行业硬度预测机器学习模型的建立

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来源期刊

International Journal of Industrial Engineering and Management Business, Management and Accounting-Business, Management and Accounting (miscellaneous)

CiteScore

5.00

自引率

17.20%

发文量

审稿时长

21 weeks

期刊介绍： International Journal of Industrial Engineering and Management (IJIEM) is an interdisciplinary international academic journal published quarterly. IJIEM serves researchers in the industrial engineering, manufacturing engineering and management fields. The major aims are: To collect and disseminate information on new and advanced developments in the field of industrial engineering and management; To encourage further progress in engineering and management methodology and applications; To cover the range of engineering and management development and usage in their use of managerial policies and strategies. Thus, IJIEM invites the submission of original, high quality, theoretical and application-oriented research; general surveys and critical reviews; educational or training articles including case studies, in the field of industrial engineering and management. The journal covers all aspects of industrial engineering and management, particularly: -Smart Manufacturing & Industry 4.0, -Production Systems, -Service Engineering, -Automation, Robotics and Mechatronics, -Information and Communication Systems, -ICT for Collaborative Manufacturing, -Quality, Maintenance and Logistics, -Safety and Reliability, -Organization and Human Resources, -Engineering Management, -Entrepreneurship and Innovation, -Project Management, -Marketing and Commerce, -Investment, Finance and Accounting, -Insurance Engineering and Management, -Media Engineering and Management, -Education and Practices in Industrial Engineering and Management.