Abstract: A system may include an electronic device such as a head-mounted device and a handheld controller for controlling the electronic device. The handheld controller may have a housing with an elongated shaft extending between first and second tip portions. The handheld controller may have power receiving circuitry configured to receive power from a power source. The power source may be incorporated into an electronic device such as a wireless charging dock or stick, a battery case, or a head-mounted device. The power source may supply power through terminals that form ohmic contacts with mating terminals in the finger device or may transmit power wirelessly using capacitive coupling or inductive charging arrangements. Magnets may be used to hold and align the elongated shaft of the handheld controller on the power source.

Abstract: The solution from a multivariable predictive controller (MPC) is analyzed and described by providing quantitative input to operators regarding the effect of changing controller limits on the MPC controller solution. This information allows a rapid operator response to changes and more optimal process operation.

Abstract: The present invention is a method for detecting an abnormal event for process units of an ethylene processing system. The method compares the operation of the process units to a model developed by principal components analysis of normal operation for these units. If the difference between the operation of a process unit and the normal operation indicates an abnormal condition, then the cause of the abnormal condition is determined and corrected.

Abstract: The solution from a multivariable predictive controller (MPC) is analyzed and described by providing quantitative input to operators regarding the effect of changing controller limits on the MPC controller solution. This information allows a rapid operator response to changes and more optimal process operation.

Abstract: Thousands of process and equipment measurements are gathered by the modern digital process control systems that are deployed in refineries and chemical plants. Several years of these data are historized in databases for analysis and reporting. These databases can be mined for the data patterns that occur during normal operation and those patterns used to determine when the process is behaving abnormally. These normal operating patterns are represented by sets of models. These models include simple engineering equations, which express known relationships that should be true during normal operations and multivariate statistical models based on a variation of principle component analysis. Equipment and process problems can be detected by comparing the data gathered on a minute by minute basis to predictions from these models of normal operation.