Development of a Blockchain-Based System for Real-Time Chemical Process Data Integrity

Thesis Abstract

The integrity and security of real-time chemical process data are paramount for ensuring operational safety, regulatory compliance, and process optimization within the chemical manufacturing industry. However, existing data management systems often face challenges related to data tampering, unauthorized access, and difficulties in ensuring data traceability, which compromise decision-making and pose significant safety risks. This study aims to develop a blockchain-based system designed to enhance the integrity, transparency, and traceability of real-time chemical process data. The specific objectives include examining current data management practices, designing a blockchain architecture suitable for chemical process applications, implementing a prototype system, and evaluating its effectiveness in real-world scenarios. A mixed-method research design was adopted, integrating qualitative analyses of current data management practices with quantitative experiments to assess the prototype's performance. The study population comprised 15 chemical manufacturing plants across the country, with a purposive sample of 10 plants selected based on their operational scale, data management maturity, and willingness to participate. Data collection instruments included structured interviews with plant managers and data engineers, system simulation tools, and real-time data feeds from the plants’ process control systems. The qualitative data from interviews were analyzed thematically to understand pain points and user requirements, while quantitative data from system implementation were analyzed using descriptive statistics, regression analysis, and ANOVA to determine system performance metrics such as processing latency, data integrity verification rates, and user satisfaction levels. The proposed blockchain architecture leverages a hybrid model integrating permissioned blockchain networks with smart contract capabilities to authenticate, timestamp, and securely log data entries from multiple sensors and control systems. The prototype was implemented using Hyperledger Fabric, incorporating cryptographic techniques for data encryption and multicast consensus algorithms to enhance scalability and resilience. The system was tested with a sample of 20,000 data points collected over a three-month period across participating facilities. Results are anticipated to demonstrate substantial improvements in data verifiability, with an expected reduction in tampering incidents by over 85%, improved auditability, and enhanced real-time data accuracy. Additionally, the study hypothesizes that blockchain deployment will lead to increased user confidence and operational efficiency, as evidenced by higher satisfaction ratings and faster data retrieval times compared to traditional systems. This research contributes novel insights into the application of blockchain technology within chemical process industries, filling gaps related to real-time data security and integrity in collaborative manufacturing environments. It extends existing theoretical frameworks, notably the Information Security Trust Model and the Technology Acceptance Model, to contextualize blockchain adoption in high-stakes industrial settings. The study also provides a detailed conceptual model illustrating the integration process, data flow, and security protocols suitable for complex chemical processing environments. The main conclusion underscores that blockchain technology offers a viable solution for securing and verifying real-time chemical process data, thereby improving safety, compliance, and operational decision-making. Recommendations include the further scaling of the prototype to accommodate larger industrial networks, integration with existing enterprise resource planning systems, and the development of standardized protocols for blockchain deployment in chemical manufacturing. Future research should explore the longitudinal impacts of blockchain implementation on data governance practices and include cost-benefit analyses to facilitate broader industrial adoption. Overall, this study advances the understanding of how emerging digital technologies can transform data security paradigms in process engineering contexts.