The use of non-noble metal catalyst as electrode for energy harvesting device have drawn great deal of attention owing to its distinct features. In this work, cobalt oxide has been directly fabricated on carbon cloth substrates using simple cost effective Successive Ionic Layer Adsorption and Reaction. Cobalt oxide synthesized from Co (II) nitrate and NaOH was used as the electrode for generation of electricity from dairy wastes using Microbial Fuel Cells (MFC). Electrochemical characteristics such as cyclic voltammetry have been carried out for the cobalt oxide/carbon cloth and the obtained results are found to be a good alternative for platinum catalyst. A current of 0.15 mA was obtained at an external resistance of 2 kΏ. A single cell prototype of double chamber MFC is designed and the performance analysis is carried out in this work.

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.