# Bioreactors: Applications and Innovations for a Sustainable and Healthy Future--A Critical Review
## 3 Challenges in Monitoring Bioprocesses in Bioreactors
In the bioprocessing sector, many researchers are integrating artificial intelligence (AI) into membrane bioreactors for a range of applications, including biohydrogen production, wastewater treatment process control, identification of critical factors in membrane fouling, and filtration process analysis. Various AI models can establish correlations between key bioprocess parameters such as temperature, time, agitation, pH, pressure, nitrogen concentration, CO\({}_{2}\) levels, biological and chemical oxygen demand, dissolved oxygen, suspended solids, volatile suspended solids, total dissolved solids, total organic carbon, and volatile organic compounds. This capability allows researchers to gain preliminary insights into potential optimized scenarios without requiring significant time or resource investment [45; 47]. There are numerous AI models available for such tasks, each with unique operating mechanisms and outcomes. These models include Artificial Neural Network (ANN), Bayesian Network (BN), Elman Neural Network (ENN), Feed Forward Neural Network (FFNN), Fuzzy Inference System (FIS), Model Tree (MT), Multilayer Perceptron (MP), Radial Basis Function (RBF), Wavelet Neural Network (WNN), Long Short-Term Memory(LSTM), Self-Organizing Map (SOM), Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), Backpropagation (BP), Genetic Algorithm (GA), Random Forest (RF), Artificial Bee Colony Optimization (ABC), and Recurrent Neural Network (RNN) [48, 49]. Given the distinct results produced by each model, they are often studied individually or in combination. Among the most widely applied models in bioprocesses are Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Long Short-Term Memory (LSTM), and Adaptive Neuro-Fuzzy Inference Systems (ANFISs) [45, 47] (Table 3). \begin{table}
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**Models** & **Description** & **Applications** & **References** \\ \hline \multirow{8}{*}{Artificial Neural Network (ANN)} & The Artificial Neural Network (ANN) is a computational model inspired by the functioning of biological neural networks, structured using algorithms across multiple layers of neurons and an output layer. Each node in the network receives input data, processes it through an activation function weighted by coefficients (weights), and transmits the results to the subsequent layer & Study of the effects of hydrogen bonding in the gel fouling phenomenon; prediction of membrane fouling from long-term anoxic-aerobic MBR operational data, To study the membrane fouling behaviors in aerated MBR, it predicts transmembrane pressure in the anaerobic membrane bioreactor-sequencing batch reactor during biohydrogen production, identifying critical fouling factors and predicting fouling behavior in anaerobic membrane bioreactors. & Study of the effects of hydrogen bonding in the gel fouling phenomenon; prediction of membrane fouling from long-term anoxic-aerobic MBR operational data, To study the membrane fouling behaviors in aerated MBR, it predicts transmembrane pressure in the anaerobic membrane bioreactor-sequencing batch reactor during biohydrogen production, identifying critical fouling factors and predicting fouling behavior in anaerobic membrane bioreactors. & [45, 49, 50, 51] \\ \cline{1-1}  & The Long Short-Term Memory (LSTM) is a specific architecture of recurrent neural networks (RNNs). Its distinction lies in its specialization for addressing the problem of long-term dependencies in sequential data. & Predictive Control with Rationality Verification for Bioreactors in Wastewater Treatment, predicting biogas production from large-scale anaerobic digesters to predict the nutrient removal efficiency in sewage treatment. & [45, 52, 53] \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \end{tabular}
\end{table} 
Table 3: Artificial intelligence models and their applications in bioreactors. Figure 2: Schematic of an on-line and off-line monitoring system.