Abstract
Industrial wastewater contains excessive micro insoluble solids (MIS) that probably cause environmental pollutions. Near-infrared (NIR) spectroscopy is an advanced technology for rapid detection of the complex targets in wastewater. An Internet of Things (IoT) platform would support intelligent application of the NIR technologies. The studies of intelligent chemometric methods mainly contribute to improve the NIR calibration model based on the IoT platform. With the development of artificial intelligence, the backward interval and synergy interval techniques were proposed in combination use with the least square support vector machine (LSSVM) method, for adaptive selection of the informative spectral wavelength variables. The radial basis function (RBF) kernel is applied for nonlinear mapping. The regulation parameter and the kernel width are fused together for smart optimization. In the design for waveband autofittings, the total of digital wavelengths in the full scanning range was split into 43 equivalent subintervals, and then, the back interval LSSVM (biLSSVM) and the synergy interval LSSVM (siLSSVM) models were both established for the improvement of prediction results based on the adaptive selection of quasidiscrete variable combination. In comparison with some common linear and nonlinear models, the best training model was acquired with the siLSSVM method while the best testing model was obtained with biLSSVM. The intelligent optimization of model parameters indicated that the proposed biLSSVM and siLSSVM deep learning methodologies are feasible to improve the model prediction results in rapid determination of the wastewater MIS content by the IoT-based NIR technology. The machine learning framework is prospectively applied to the fast assessment of the environmental risk of industrial pollutions and water safety.
Highlights
Internet of Things (IoT) has gradually permeated all kinds of technological landscape, such as monitoring, agriculture, irrigation management, healthcare, and security [1]
The IoT-based NIR spectroscopic technology was utilized for quantitative detection of wastewater micro insoluble solids (MIS) contents
All of the intervals were tested for least square support vector machine (LSSVM) models in the way of backward interval removal and synergy interval combination
Summary
Internet of Things (IoT) has gradually permeated all kinds of technological landscape, such as monitoring, agriculture, irrigation management, healthcare, and security [1]. Intelligent spectral detection based on the IoT platform supports for sustainable eco-environment in combination with machine learning methodologies. With the development of chemometric studies, NIR technology has become a strong analytical tool for rapid quantitative determination of chemical components and parametric properties for water samples. Given the success of these previous works, we hypothesize that similar methodologies of NIR calibration would be able to predict the wastewater MIS for rapid assessment of environmental risk of water pollution. This study was conducted with industrial wastewater samples collected in south China, to establish intelligent analytical models for estimating the MIS content by using the IoT-based NIR technology. The proposed methodological framework is expected to build up smart learning models for precise assessment of the prospective pollution level of wastewater, providing governance guidelines for environment sustainable with synchronous development in the development of the IoT cloud platform
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