Abstract: Disclosed are methods for scheduling when to run a process to take advantage of time-varying input prices by predicting the “rank” of the current actual input price among future input prices. The rank is that portion of a future time period during which the input price will be less than the current actual input price. An “operational cutoff” is set based on the portion of time during which a process should run in order to produce a target output amount. Periodically, the actual input price is determined, and its rank is predicted. The predicted rank is then compared with the operational cutoff. If the predicted rank is below the cutoff, then it makes economic sense to run the process. Otherwise, it would be cheaper not to run the process at present but to wait a while in expectation that the input price will drop.

Abstract: Disclosed are methods for scheduling when to run a process to take advantage of time-varying input prices by predicting the “rank” of the current actual input price among future input prices. The rank is that portion of a future time period during which the input price will be less than the current actual input price. An “operational cutoff” is set based on the portion of time during which a process should run in order to produce a target output amount. Periodically, the actual input price is determined, and its rank is predicted. The predicted rank is then compared with the operational cutoff. If the predicted rank is below the cutoff, then it makes economic sense to run the process. Otherwise, it would be cheaper not to run the process at present but to wait a while in expectation that the input price will drop.

Abstract: Disclosed is a system that adjusts the operational parameters of a fiber filter apparatus to keep measured properties indicative of paper quality, such as the paper's porosity, within desired value ranges. A feedback loop may be set up in which input properties are read, such as the paper's porosity and the pollution content of the filtrate leaving the fiber filter. These inputs go into a predictive model that determines how to adjust the fiber filter's operational parameters in order to move the measured paper quality properties into their desired value ranges while accommodating other constraints on the fiber filter's operation. The model looks at positive and negative correlations between the operational parameters and the measured inputs. Having processed the inputs and predicted an outcome, the predictive model directs controllers to change the operational parameters of the fiber filter. The input properties are again read, and the feedback loop is repeated.