Sunil Shirish Shah

Design of Controllable Batch Processes in the Presence of Uncertainty

Design of controllable batch processes can be more challenging than continuous processes because of their unsteady nature of operation. The operating strategy of a batch process is characterized by trajectories of manipulated variables. This precludes the use of conventional controllability measures in evaluating the controllability of a given batch process design. Short process development cycles typically accredited to batch processes lead to uncertainty in the model formulation. Integrated approach to batch process design and control addresses the problem of controllability of a batch process during the design phase. This is best achieved by treating the problem as a dynamic optimization problem with time invariant (design) and time variant (operating) variables.The method proposed in this paper uses the decomposition feature of Generalized Benders Decomposition (GBD) to evolve a 2-level nested optimization problem (primal and master), one involving time variant decision (operating) variables and the other involving time invariant decision (design) variables. To enhance the computational efficiency, a relaxed LP formulation of the master problem is proposed. This variant of GBD, termed as ExGBD, is guaranteed to converge to the optimum for convex problems. A simple batch reactor design problem has been chosen to demonstrate ExGBD.

Predictive Heat Exchanger Efficiency Monitoring

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.© 2005 ASME

Predictive Performance Assessment of Heat Exchangers for Proactive Remediation

Efficiency of industrial heat exchangers degrades over time as a result of deposition of the extraneous matter present in the heat-exchanging medium. Many times the degradation is rapid enough to take a heavy toll on the overall plant economy. It is therefore important to keep a closer watch on heat exchanger performance so that a suitable corrective action can be initiated the moment any abnormality is detected. While most plants monitor their exchangers to some degree, ability to effect real and sustainable improvements requires four components: (1) near-real time detection capability; (2) an early warning system (3) capability to diagnose the cause of fouling; and (4) right expertise to treat the cause in order to slow or reverse the degradation. CHeX2 is a comprehensive tool that we have developed, which monitors, predicts, and diagnoses heat exchanger performance. This tool has been designed to cover a wide range of heat transfer equipments namely simple heat exchangers, condensers and partial condensers with both the shell and tube-side condensation. The unique features of this advanced tool include: numerous data cleaning steps to enhance 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. A trained field engineer takes the final control action by 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.