Anaerobic digestion is widely used to treat highstrength wastewater and produces methane as a by-product for power generation. Treatment and reuse of industrial effluent also contribute to water conservation efforts. Nevertheless, the sensitivity of anaerobic digestion system proves to be a challenge in ensuring consistent quality of treated wastewater and biogas production. Hence, it is essential to devise an effective model and control system that accurately represent the dynamics of anaerobic digestion and can respond to changes in process parameters with proper fault detection and output prediction. This article provides a comprehensive review on (1) the anaerobic digester technology and parameters governing its efficiency, (2) mechanistic and meta-heuristic models used to describe this process, and (3) the process control strategies. In this study, adaptive controller was found to be able to provide wider options in terms of controlled and manipulated variables. Nevertheless, an in-depth study is essential to determine the best controller to be applied for a particular system where further optimization can be done to achieve the best performance.
In the recent past Brain Computer Interface (BCI) has become popular in the field of rehabilitation engineering for physically challenged people to improve their day-to-day activities independently. A proper BCI can possibly be achieved by proper classification and feature extraction techniques from the Electroencephalogram (EEG) data acquired from the brain.
In this paper time domain (TD) features, like Mean Absolute Value (MAV), Zero Crossings (ZC), Slope Sign Changes (SSC) and Waveform Length (WL) is considered for classification of six channels of EEG data with time window of size 1-sec containing 250 data with an overlap of 125 data. A pair-wise combination of five different mental tasks has been considered for classification using Linear Discriminate Analysis (LDA) for seven subjects.Classification accuracies ranging from 67%-100% is obtained for pair-wise classification. The classification accuracy with TD features is found to be considerably increased besides reduction in the memory space and processing time of the classifier used in BCI applications.
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