Assessment of Waste Heat Recovery Efficiency in Commercial Chemical Plants

Thesis Abstract

In the face of escalating energy costs and increasing environmental concerns, the efficient utilization of waste heat in chemical processing units has gained critical importance for sustainable industrial operation. Despite the recognition of waste heat recovery (WHR) as a viable strategy to enhance energy efficiency and reduce greenhouse gas emissions, a comprehensive understanding of the current efficiencies and challenges faced by commercial chemical plants remains limited. This study aims to empirically assess the waste heat recovery efficiency in operational chemical manufacturing facilities, with a focus on identifying significant factors influencing recovery performance and proposing optimization strategies. The specific objectives include quantifying the extant waste heat recovery efficiencies across selected plants, evaluating the technical and operational barriers to optimal recovery, and developing a predictive model for efficiency enhancement. The research adopts a mixed-methods approach, combining quantitative field measurements with qualitative assessments. The quantitative component involves collecting thermal data from a sample of 15 commercial chemical plants located within a specific industrial region, selected through stratified random sampling to ensure representative variability in plant size, production processes, and age. Data collection instruments include portable infrared thermography cameras and heat flux sensors, which are used to capture temperature profiles and heat loss metrics over a six-month operational period to account for seasonal variations. The qualitative aspect involves semi-structured interviews with plant engineers and maintenance personnel, providing insights into operational practices, equipment conditions, and perceived barriers to effective heat recovery. Data analysis employs a combination of statistical and analytical techniques. Descriptive statistics will summarize the heat recovery performance metrics, while multiple regression analysis will examine the influence of variables such as boiler efficiency, heat exchanger design, process temperature ranges, and operational schedules on recovery effectiveness. The use of Analysis of Variance (ANOVA) will facilitate comparison across different plant categories. Additionally, a conceptual model based on the Theory of Constraints (TOC) will be developed to identify and mitigate bottlenecks within the heat recovery systems. Expected findings suggest that the average waste heat recovery efficiency in the sampled plants ranges from 35% to 65%, with significant variability attributable to equipment age, operation practices, and process integration. The study anticipates revealing critical technical gaps such as suboptimal heat exchanger configurations and sensor placement, as well as operational challenges like inconsistent process temperatures and maintenance lapses. Furthermore, the research is expected to establish correlations between operational parameters and recovery efficiency, enabling the formulation of a predictive framework for plant performance improvement. The study’s contribution to knowledge lies in providing the first comprehensive empirical assessment of waste heat recovery efficiencies across a representative sample of commercial chemical plants, complemented by an analytical model to optimize performance. It underscores the importance of integrating thermodynamic analysis with operational management and offers actionable recommendations for plant managers and policymakers to enhance recovery systems. These include adopting advanced heat exchanger technologies, improving maintenance regimes, and implementing real-time monitoring systems based on the developed model. Conclusively, the research advocates for adopting a holistic, data-driven approach to maximize waste heat recovery potentials, thereby reducing operational costs and environmental impacts. It recommends further studies focusing on the integration of emerging technologies such as advanced heat pumps and artificial intelligence-based control systems to elevate recovery efficiencies beyond current benchmarks, fostering sustainable industrial practices. The findings provide a critical stepping stone toward refining energy management strategies within the chemical industry and contribute significantly to the theoretical understanding of thermal system optimization in complex operational environments.