Smart IoT-enabled Monitoring System for Real-Time Chemical Process Optimization

Thesis Abstract

The increasing complexity of chemical processing operations and the imperative for enhanced efficiency, safety, and sustainability underscore the critical need for advanced monitoring solutions in the chemical industry. Traditional process control systems often suffer from limitations in real-time data acquisition, leading to suboptimal decision-making and increased operational costs. This study aims to develop and evaluate a smart Internet of Things (IoT)-enabled monitoring system designed to optimize chemical processes through real-time data collection, analysis, and control. The specific objectives include designing an integrated IoT framework for chemical process monitoring, implementing sensor networks for key process parameters, developing predictive analytics models for process optimization, and assessing system performance in operational environments. Adopting a mixed-methods research design, this study combines quantitative experimental evaluation with qualitative system usability assessment. The quantitative component involves deploying the developed monitoring system in a chemical manufacturing plant with a sample population of 30 process units over a six-month period, collecting real-time data on temperature, pressure, flow rate, and chemical composition through IoT sensors. Data collection instruments comprise wireless sensor nodes equipped with high-precision transducers and a centralized cloud-based data analytics platform. The selection of sensors was informed by industry standards and validated for accuracy and reliability through calibration procedures. Data analysis techniques include multiple regression analysis, time-series analysis, and machine learning algorithms such as support vector machines (SVM) for predictive modeling, facilitated by software tools like MATLAB and Python. The qualitative aspect employs thematic analysis of user feedback obtained via semi-structured interviews and usability surveys from operators and engineers involved in system operation. This integrated approach ensures comprehensive evaluation of both technical performance and user acceptance. The system’s analytical framework is rooted in control theory and the Diffusion of Innovations theory, providing theoretical grounding for system design and user adoption strategies. Expected findings elucidate that the IoT-enabled monitoring system significantly improves process efficiency, reduces energy consumption, and enhances safety by enabling predictive maintenance and anomaly detection. Quantitative results are anticipated to demonstrate that the predictive models achieve an accuracy exceeding 90% in detecting process deviations, leading to earlier interventions and cost savings. Qualitative feedback is expected to highlight high user satisfaction, ease of system integration, and barriers to adoption that can be addressed through targeted training and interface improvements. The study contributes to existing knowledge by demonstrating an effective integration of IoT and advanced analytics tailored for chemical process environments, thereby filling gaps related to scalable, real-time process optimization solutions. It provides a replicable framework for industry practitioners seeking to leverage digital transformation initiatives in process industries. Furthermore, the research advances theoretical understanding by applying control principles and adoption theories in the context of Industry 4.0-enabled chemical manufacturing. In conclusion, this research affirms that IoT-driven monitoring systems can markedly enhance chemical process performance and safety. It recommends that chemical plants adopt integrated IoT solutions, invest in sensor infrastructure, and foster training programs to maximize benefits. Future studies should explore the scalability of such systems across different chemical sectors, incorporate enhanced cybersecurity measures, and evaluate long-term economic impacts. Overall, this work demonstrates a significant step toward intelligent, data-driven chemical process management and underscores the importance of technological innovation in achieving sustainable industrial development.