Quantitative analysis on the oil content of oilfield wastewater based on a convolutional neural network model and ultraviolet transmission spectroscopy

Wang Q., Li H., Zhao H., Zhang X., Arici M., Li H.

WATER SCIENCE AND TECHNOLOGY, vol.87, pp.1779-1790, 2023 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 87
  • Publication Date: 2023
  • Doi Number: 10.2166/wst.2023.097
  • Journal Name: WATER SCIENCE AND TECHNOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Analytical Abstracts, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chimica, Compendex, EMBASE, Environment Index, Geobase, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Directory of Open Access Journals
  • Page Numbers: pp.1779-1790
  • Keywords: CNN, deep learning, oil content, oilfield wastewater, UV spectroscopy, RECOGNITION, GAS
  • Kocaeli University Affiliated: Yes

Abstract

Oil content (OC) is one of the important evaluation indicators in oilfield wastewater (OW) treatment. The purpose of this study is to realize online real-time detection of OC in OW by combining ultraviolet spectrophotometry with the convolutional neural network (CNN). In this paper, 80 groups of OW transmission data were measured for model establishment. Three CNN models with different structures are estab-lished to generalize the super parametric optimization process of the model. Furthermore, as a common method used in spectroscopy, the synergy interval partial least squares (siPLS) model is built in order to compare its accuracy with the CNN model. The results indicated the CNN model has a better performance than siPLS, in which the CNN model numbered Model 3 has the lowest root mean square error (MSE) of prediction (RMSEP) of 1.606 mg/L. As a consequence, the CNN model can be used in the monitoring of OW. This article will guide a rapid analysis of the OC of OW.