# Bioreactors: Applications and Innovations for a Sustainable and Healthy Future--A Critical Review
## 3 Challenges in Monitoring Bioprocesses in Bioreactors  
Sensors and operational control systems in a bioreactor are vital for monitoring and maintaining controlled conditions, thereby enhancing the efficiency of chemical, biochemi  
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**Reactor Type** & **Mass Transfer** & **Heat Transfer** & **Shear Stress** & **Medium Homogeneity** & **Scalability** & **Oxygen Transfer** & **References** \\ \hline \multirow{4}{*}{Stirred Tank Reactor (STR)} & \multirow{4}{*}{High, mechanical agitation facilitates efficient transfer (1) High  
cal, and biological processes. Despite their importance, significant challenges remain in monitoring and controlling bioprocesses due to the diversity of bioreactor models and biological models. As far as reactors are concerned, there are batch reactors, fed-batch reactors, continuous bioreactors, magnetic bioreactors, perfusion bioreactors, wave bioreactors, pneumatic bioreactors, rotating wall bioreactors, stirred bioreactors, and more recently, microfluidic reactors [36; 37]. The engineering differences in these bioreactors, both in physical structure and operational mechanisms, lead to varying sensor applications [37]. Regardless of the specific type of bioreactor, several basic operational parameters must be measured. These include pH, temperature, pressure, feed flow rate, output flow rate, agitation, oxygen level, CO\({}_{2}\) level, and N\({}_{2}\) level. Additionally, there are parameters specific to each bioprocess, such as cell growth, enzyme release, nutrient metabolite levels, and secondary metabolite concentration. These parameters can vary based on the type of cell or microorganism used, the metabolic pathway employed (aerobic or anaerobic), the product of interest (which could be the cell itself or an extra- or intracellular product), and cell viability [38; 39; 40]. Sensors are also crucial for monitoring substances harmful to bioprocesses, such as endotoxins, contaminant bacteria, and cell culture stress. A significant current challenge is developing in-line or at-line sensors, including optical and electrical sensors, to measure cell growth, cell density, cell viability, enzyme release, nutrient metabolites, and secondary metabolite concentration. Many of these analyses are performed using spectroscopic techniques, which, although well-established, require specialized equipment and are often not integrated into bioreactors and are thus classified as off-line monitoring [41; 42]. The aim of new sensor technologies is to create monitoring methods that can replace spectroscopic techniques, enabling continuous, integrated monitoring that can be performed remotely. This shift to automatic measurements reduces human operational errors and the risk of contamination, which can render a bioprocess unfeasible. An additional advantage of an online monitoring model is the constant flow of information for mathematical adjustment models, allowing for more robust, precise control and automated conditioning of bioreactor parameters. Figure 2 shows a diagram of an online and offline monitoring system [36; 43; 44]. Artificial intelligence (AI) has become a pivotal tool for controlling and monitoring bioreactor processes. With advancements in computing and programming, it is now possible to create sophisticated analysis algorithms that leverage extensive databases of pre-existing information or real-time data gathered during process monitoring [27; 45]. AI's learning capabilities, combined with its ability to rapidly respond to critical variables, enable precise adjustments, ensuring that processes operate at optimal or near-optimal conditions. Beyond operational control, AI is extensively used to develop protocols, optimize workflows, and correlate large data sets. This allows researchers and engineers to identify the conditions under which processes achieve peak performance, enhancing overall efficiency and productivity [27; 45; 46].