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Precise control of biological wastewater treatment for nitrogen removal is difficult because of the nonlinearity, time-varying, and time-consuming nature of the process. With due emphasis on addressing the challenges involved in its effective implementation, this study developed an artificial neural network (ANN) based soft sensor (SS) with a set of proposed thumb rules for online forecasting of the concentrations of hard-to-measure parameters (NH4 + and NO2 −) from secondary easy-to-measure variables, (reactor volume, dissolved oxygen, suspended solids, pH, temperature, and ORP) in an Anammox based pilot plant. Four hybrid neural networks (PCA-Kalman NN, PCA NN, Kalman NN, and Non NN) were applied to identify net optimum input vectors for the SS, using an appropriate quantity of samples from the set of secondary variables. The proposed hybrid SS was tested on a sewage wastewater treatment plant operated using a Matlab R2018a framework and validated using operational plant data. The results showed that the PCA-Kalman neural network with R 2 values of 0.9985 and 0.9263 for NH4 + and NO2 –, respectively, is potentially a valuable tool for plant operators in the selection of operational states to minimize cost and to efficiently predict important parameters that are prone to errors due to a failure in online sensors.

Detecting early signs of plant diseases and pests is important to preclude their progress and minimize the damages caused by them. Many methods are developed to catch signs of diseases and pests from plant images with deep learning techniques, however, detecting early signs is still challenging because of the lack of datasets to train subtle changes in plants. To solve these challenges, we built an automatic data acquisition system for the accumulation of a large dataset of plant images and trained an ensemble model to detect targeted plant diseases and pests. After obtaining 13,393 plant image data, our ensemble model shows a decent detection performance with an average of AUPRC 0.81. Also, this data acquisition and the detection process can be applied to other plant anomalies with the collection of additional data.

The anammox process, used to remove nitrogen from wastewaters is conside red a promising approach due to its advantages over traditional processes. The current study emphasizes on the cost effective nitrogen removal from the sidestream effl uent of anaerobic digester with partial nitration (PN) and anaerobic ammonium oxidation (anammox) process for the municipal wastewater treatment plant. The main objective of this study was to model a cost effective strategy for setting up a lab-scale sequencing batch reactor (SBR) by using activated sludge model (ASM) process equations with applying novel control strategies (NCS) for improving nitrogen-removal effi ciency (NRE). An average rate of removal 80% was obtained during the period of its operation. NCS (intermittent aeration, alteration in the cycle length, etc) were introduced to optimize the operating cost. The overall system contributes to lowering in the greenhouse gas emissions by minimizing the use of energy (60-65%) and hence supporting the WHO mission of achieving sustainable development goals. Results further indicate the future possibility of escalating the lab-scale to full-scale applications.

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