Development of IoT-Enabled Sensors for Real-Time Monitoring of Industrial Chemical Processes

Thesis Abstract

Industrial chemical processes are integral to manufacturing sectors, yet their efficiency, safety, and environmental impact are often compromised by limited real-time monitoring capabilities. Conventional sensor systems are frequently hindered by delayed data acquisition, limited sensitivity, and susceptibility to harsh process conditions, thereby impeding prompt decision-making and process optimization. This study aims to develop and evaluate Internet of Things (IoT)-enabled sensor systems tailored for real-time monitoring of critical chemical parameters within industrial environments, specifically focusing on temperature, pH, concentration levels, and pressure. The research objectives include designing hybrid sensor prototypes leveraging nanomaterial-based sensing elements, integrating these sensors with IoT communication modules, and assessing their performance in operational chemical processes. Employing a mixed-methods approach, the research adopts an exploratory sequential design. The quantitative phase involves the development of sensor prototypes and their deployment in a polymer manufacturing plant with a population of 50 process units. A sample of 20 units, selected through stratified random sampling to ensure process variability, will be monitored over an 8-month period. Data collection instruments include customized sensor nodes, data loggers, and IoT gateways, with data transmitted via Wi-Fi and cellular networks. Sensor calibration and validation will be carried out through laboratory analyses, with the laboratory reference methods including Atomic Absorption Spectroscopy (AAS) and Fourier Transform Infrared Spectroscopy (FTIR). The analysis encompasses regression models to evaluate sensor accuracy, ANOVA to compare sensor performance across different process conditions, and time-series analysis to assess data consistency and trend detection. The qualitative phase involves semi-structured interviews with process engineers and safety officers, analyzed through thematic analysis to capture insights on system usability, operational challenges, and safety implications. Key anticipated findings include the identification of sensor configurations that achieve high sensitivity (>95%) and stability under high-temperature, corrosive environments; a significant correlation (p < 0.05) between sensor readings and laboratory reference values; and the demonstration of the sensors' capacity to promptly detect deviations in chemical parameters, enabling predictive maintenance and process control. The integration of IoT platforms is expected to facilitate real-time dashboards that improve decision-making efficiency, reduce process downtime by an estimated 15%, and enhance safety protocols. This research contributes to the body of knowledge by establishing a benchmark for IoT-enabled sensor systems in industrial chemistry, providing empirical evidence of their operational viability and quantitative performance metrics. It adapts theoretical models such as the Technology Acceptance Model (TAM) and the Sensor Performance Index (SPI) to interpret user acceptance and sensor efficacy within industrial contexts. The study concludes that IoT-enabled sensors are viable tools for transforming chemical process monitoring, with implications for improved process safety, quality assurance, and environmental compliance. Recommendations include scaling the deployment of sensor networks across multiple process units, investing in sensor communication security, and fostering multidisciplinary collaborations to further refine sensor materials and IoT integration strategies. Future research should explore machine learning algorithms for predictive analytics, and long-term durability studies to ensure sensor reliability over extended operational periods.