Optimal load allocation of multiple fuel boilers.

This paper presents a new methodology for optimally allocating a set of multiple industrial boilers that each simultaneously consumes multiple fuel types. Unlike recent similar approaches in the utility industry that use soft computing techniques, this approach is based on a second-order gradient search method that is easy to implement without any specialized optimization software. The algorithm converges rapidly and the application yields significant savings benefits, up to 3% of the overall operating cost of industrial boiler systems in the examples given and potentially higher in other cases, depending on the plant circumstances. Given today's energy prices, this can yield significant savings benefits to manufacturers that raise steam for plant operations.

Digital redesign of analog smith predictor for systems with input time delays.

This paper presents a new methodology for digitally redesigning an existing analog Smith predictor control system, such that the cascaded analog controller with input delay can be implemented with a digital controller. A traditional analog Smith predictor system is reformulated into an augmented system, which is then digitally redesigned using the predicted intersampling states. The paper extends the prediction-based digital redesign method from a delay free feedback system to an input time-delay cascaded system. A tuning parameter v is optimally determined online such that in any sampling period, the output response error between the original analogously controlled time-delay system and the digitally controlled sampled-data time-delay system is significantly reduced. The proposed method gives very good performance in dealing with systems with delays in excess of several integer sampling periods and shows good robustness to sampling period selection.