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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.

Activated sludge treatment is one of the most widely used processes for wastewater treatment (WWT). These systems are built with sufficient design margin to allow changes in loading and process conditions. This is necessary and prudent to overcome limitations in measurement, monitoring and controlling of WWT process parameters at the desired frequency. Online sensors for mixed liquor suspended solids, chemical oxygen demand (COD), nitrogen, phosphorus, and other parameters available today are limited in application due to high cost and low reliability. Hence, many of the parameters are measured off-line when needed. This paper provides a framework to estimate parameters on-line using limited and delayed measurements. The proposed approach is based on the design of a Bayesian filter such as an extended Kaiman filter (EKF), which measures and controls membrane bioreactor system using limited and delayed measurements. The objective is to estimate the states and parameters with limited and delayed measurements. Simulations show the efficacy of the proposed approach.

Growing environmental concerns and shrinking water resources require methods beyond conventional wastewater treatment. Membrane bioreactor (MBR) is a technology that has become a ubiquitous choice for high quality treatment and reuse of wastewater. One of the key challenges in wastewater treatment is the high energy cost associated with aeration. MBR systems use feedback control to regulate the measured dissolved oxygen level at a predetermined set point by manipulating the blower throughputs. However, for high dynamic loads, feedback control may not result in the best performance and energy efficiency. Any attempt to optimize performance and power consumption beyond a simple controls strategy requires a proper trade-off analysis between investments on additional sensors and the long-term benefits. This article proposes a joint state and parameter estimation methodology, which measures and controls the MBR system using available measurements. Thus, limitations of feedback strategies can be overcome by predicting the impact of time varying disturbances on the outputs. The novelty in this approach is the ability to reconstruct the unknown states and parameters with available measurements. The unknown parameters are adaptively estimated online. Lyapunov's direct method is employed to show boundedness of state and parameter estimation errors. Simulation results illustrate the efficacy of the approach. 

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