Abstract: Methods for building models that estimate compositional properties of a process may include (a) receiving data relating to a process; (b) cleaning the data by identifying and removing outlying data points from the data; and conditioning the data; (c) identifying inferential model parameters comprising a parameter selected from the group consisting of: a compositional property of the process as a model output that is not part of the data, operational constraints of the process, interactions between process variables of the chemical process, and any combination thereof; (d) building one or more inferential models based on the cleaned data and the inferential model parameters; and (e) outputting the one or more models and the corresponding validation metric.

Abstract: A method is presented for adjusting the steady-state gains of a multivariable predictive control, planning or optimization model with uncertainty. The user selects a desired matrix relative gain criteria for the predictive model or sub-model. This is used to calculate a base number. Model gains are extracted from the predictive model and the magnitudes are modified to be rounded number powers of the calculated base number.

Abstract: A method is presented for adjusting the steady-state gains of a multivariable predictive control, planning or optimization model with uncertainty. The user selects a desired matrix relative gain criteria for the predictive model or sub-model. This is used to calculate a base number. Model gains are extracted from the predictive model and the magnitudes are modified to be rounded number powers of the calculated base number.