BACKGROUND

The production of alkylene oxide can be accomplished via a chemical system that utilizes catalytic epoxidation of olefins in the presence of oxygen using silver based catalysts. Conventional silver-based catalysts used in such processes typically provide a relatively lower efficiency or “selectivity” (e.g., a lower percentage of the reacted alkylene is converted to the desired alkylene oxide). In some processes, when using conventional catalysts in the epoxidation of ethylene, the theoretically maximal efficiency towards ethylene oxide, expressed as a fraction of the ethylene converted, does not reach values above the 6/7 or 85.7 percent limit.

Certain such catalysts, such as so-called “high selectivity” or “high efficiency” catalysts, tend to exhibit relatively steep efficiency curves as a function of gas phase promoter concentration as the concentration moves away from the value that provides a high efficiency (e.g., the change in efficiency with respect to a change in gas phase promoter concentration is at least 0.2%/ppmv when operating away from the efficiency maximizing promoter concentration). Thus, small changes in the promoter concentration can result in significant efficiency changes as the efficiency can exhibit a pronounced maximum, e.g., an optimum, at certain concentrations (or feed rates) of the gas phase promoter over time. The pronounced maximum of the chemical process is due to multi-phase flows and chemical reactions which are characterized by process nonlinearities and time delays due to mass transport and chemical reaction rates.

As many of the variables in the chemical process have nonlinear relationships with other variables, e.g., inter-loop interaction of variables, process models need to be developed to effectively characterize these multi-interdependent variable relationships, which may also be non-linear. In various implementations, a Linear Model Predictive Control (LMPC), among other approaches, has been proposed for applications in the field of process control. However, such approaches have continued to lack stability with processes having a steady-state gain inversion.

SUMMARY

In some embodiments, a chemical system for an operation exhibiting steady-state gain inversion includes a reactor configured to receive a feed stream and produce an outlet stream to form a process and a control device configured to control a process. The control device receives inputs indicative of an operational parameter and output variables and, in response to the inputs and output variables, provides a steady-state manipulated input configured to control or optimize the process. The control device includes an input disturbance model based on using the steady-state manipulated input as a custom output measurement to determine unmeasured disturbances, a state estimator configured to utilize the custom output measurement to estimate the unmeasured disturbances entering the process and thereby predict a change in the process based on a characterization of the process by the model, a non-linear steady-state target calculator configured to determine the steady-state manipulated input for the process based on the characterization of the process as calculated by the model, and a regulator configured to provide a signal for adjustment of one or more inputs based on the steady-state manipulated input and associated output variables.

In some embodiments, a method for controlling a chemical system through a control device includes receiving inputs indicative of an operational parameter of a process and a steady-state output variable. The method also includes implementing an input disturbance model based on using an optimum manipulated steady-state input as a custom output measurement to determine the optimized manipulated input in a presence of both measured and unmeasured disturbances. In addition, the method includes estimating a state of the process to predict the process based on a characterization of the process by the model. The method further includes determining an a targeted manipulated input for the process based on the characterization of the process by the modelling and the estimating of the one or more steady-state output variables of the chemical system. Lastly, the method includes regulating the process based on the targeted manipulated input.

