Predictive Heat Exchanger Efficiency Monitoring

Abstract

The performance of heat exchangers degrades with time due to fouling or deposition of material on the heat transfer surface. The fouling of critical exchangers in manufacturing plants results in a significant cost impact in terms of production losses, energy efficiency, and maintenance costs. While most plants monitor their exchangers to some degree, the ability to effect real and sustainable improvements requires four components: (1) real time monitoring; (2) an advance warning mechanism; (3) the ability to diagnose the cause of fouling; and (4) the ability to treat the cause in order to slow or reverse the degradation. CHeX is a comprehensive tool which monitors, predicts, and diagnoses heat exchanger performance. The unique features of this advanced technology include: numerous data cleaning steps to improve data quality and isolate a net fouling trend, an adaptive model which learns from the past to predict performance three years in advance, and knowledge-based diagnostics which identify the probable cause(s) of fouling and recommend corrective actions. The final control action is performed by a field engineer in adjusting the fouling treatment. The scope of the current paper includes only the detection and prediction features. To date, CHeX has been validated at three chemical processing plants, for fourteen exchangers. Selected case studies shall be presented to demonstrate the power of its algorithms over traditional calculations.

Heuristic Framework for Sustainable Cooling Water Systems Operations in Oil Facilities

“…• The cooling water circulation pumps should be equipped with an inlet strainer to avoid impeller damage and to prevent the heat exchangers from getting plugged due to objects carry over (Prasad et al, 2005).…”

Modeling and Fouling Detection of the Aircraft Environmental Control System Heat Exchanger

“…With this information in mind, monitoring and detecting fouling in the aircraft ECS heat exchanger remains a challenge and will be tackled in chapters to follow. Several documents [95][96][97][98][99][100][101] are available on heat exchanger monitoring processes.…”

“…Model-based fault detection and diagnosis can be classified into two main categories: quantitative model-based methods or qualitative model-based methods as defined in Figure 80 (111]. There is an abundance of literature on process fault detection and diagnosis (with regards to detection of fouling) ranging from analytical methods ( [97], [98], [100], [101], and [112 -123]) to statistical approaches ( [99], and [124][125][126]). The most common methods of detecting faults in the heat exchanger are based on assessment of the heat transfer coefficient, the pressure drop, the flow rates, the temperature drop and the weight of the plates.…”

Prognosis of Component Degradation Under Uncertainty: A Method for Early Stage Design of a Complex Engineering System

“…However, statistical inference methods can be used to estimate the model parameters based on data obtained from real time monitoring of the fouling process [14].…”

“…Zubair et al [12,13] have studied statistical models to describe heat transfer component degradation. Prasad et al reported an algorithm developed by GE capable of predicting degradation in heat exchangers [14].…”