Development of IoT-Driven Monitoring System for Real-Time Chemical Reactor Optimization

Thesis Abstract

In the rapidly evolving landscape of chemical manufacturing, the optimization of chemical reactors remains a critical factor for enhancing process efficiency, safety, and sustainability. Traditional reactor monitoring methods often rely on manual data collection and off-line analysis, which can lead to delays, suboptimal operational adjustments, and increased risks of unanticipated outages or hazardous conditions. Addressing these limitations, this study aims to develop an Internet of Things (IoT)-driven real-time monitoring system to facilitate dynamic optimization and control of chemical reactors. The specific objectives include identifying key process parameters that influence reactor performance, designing an integrated sensor network for continuous data acquisition, developing predictive models for real-time decision-making, and evaluating the system’s effectiveness through experimental deployment in an industrial-scale reactor. Employing a mixed-method research design, the study integrates quantitative and qualitative approaches. The quantitative phase involves the deployment of a sensor network comprising temperature, pressure, pH, and flow sensors installed in a batch chemical reactor with a capacity of 10,000 liters, situated in a petrochemical plant. A sample size of 50 operational cycles over six months was analyzed, with data collected via wireless sensor nodes interconnected through a LoRaWAN network. Data quality assurance measures, such as calibration and redundancy checks, ensured instrument validity and reliability. The collected data were analyzed using multiple regression analysis to identify key process variables affecting yield and safety, complemented by machine learning algorithms such as support vector machines for predictive modeling of reactor conditions. The qualitative aspect involved semi-structured interviews with plant operators and process engineers to gather insights into operational challenges, user acceptance, and system integration requirements, analyzed through thematic analysis. The analytical framework incorporated the Diffusion of Innovations theory to assess user adoption potential and the control theory to model dynamic process regulation. The study also applied systems engineering principles to integrate sensor data streams with control algorithms, forming a comprehensive IoT-enabled supervisory control system. Expected findings demonstrate that the implemented IoT system significantly improves real-time visibility of reactor conditions, facilitates immediate process adjustments, and enhances overall operational efficiency. Quantitative results are anticipated to show statistically significant correlations between sensor-derived variables and reactor performance metrics (p < 0.01). The predictive models developed are expected to achieve high accuracy levels, with support vector machines delivering over 85% follower predictions of critical process anomalies. Qualitative insights are projected to reveal high acceptance among operators, provided system interfaces are intuitive and aligned with existing workflows. This research contributes novel insights into the practical integration of IoT technologies within chemical reactor environments, extending existing knowledge on real-time process monitoring and control. It demonstrates that the synergistic application of sensors, communication technologies, and machine learning algorithms can optimize reactor performance, reduce downtime, and mitigate safety hazards. The findings underpin the development of a scalable, cost-effective monitoring framework adaptable across various chemical processing industries. In conclusion, the study recommends the adoption of IoT-based monitoring systems as standard practice for chemical reactor management, emphasizing the importance of user training, cybersecurity considerations, and continual system upgrades. Future research avenues include exploring advanced data analytics, integrating AI-driven autonomous control strategies, and assessing long-term economic impacts. This work ultimately provides a robust foundation for the digitization and intelligent automation of chemical process operations, aligning with Industry 4.0 objectives and sustainability goals.