Abstract: An anti-sedimentation system can implement a method in which a pump pumps seawater to a discharge nozzle positioned under the seawater and facing an under keel clearance of a marine vessel docked at a dock. Sediments are flowed away from the under keel clearance by the discharge nozzle discharging the seawater toward the under keel clearance.

Abstract: An anti-sedimentation system can implement a method in which a pump pumps seawater to a discharge nozzle positioned under the seawater and facing an under keel clearance of a marine vessel docked at a dock. Sediments are flowed away from the under keel clearance by the discharge nozzle discharging the seawater toward the under keel clearance.

Abstract: Methods and systems to detect transient operations from abnormal operations, and to detect abnormal operations in a coker heater, include collecting on-line process data. The collected on-line process data is generated from a plurality of process variables of the process, or coker heater. A first representation of the operation of the process, or coker heater, is generated based on a first set of the collected on-line process data generated from a first set of the process variables. The first representation is adapted to be executed to generate a first result. A second representation of the operation of the process, or coker heater, is generated based on the first result and based on a second set of the collected on-line process data generated from a second set of the process variables. The second representation is adapted to be executed to generate a prediction of data generated from the second set of the process variables.

Abstract: Methods and systems to detect abnormal operations in a process of a process plant include collecting on-line process data. The collected on-line process data is generated from a plurality of dependent and independent process variables of the process, such as a coker heater. A plurality of multivariate statistical models of the operation of the process are generated using corresponding sets of the process data. Each model is a measure of the operation of the process when the process is on-line at different times, and at least one model is a measure of the operation of the process when the process is on-line and operating normally. The models are executed to generate outputs corresponding to loading value metrics of a corresponding dependent process variable, and the loading value metrics are utilized to detect abnormal operations of the process.

Abstract: Methods and systems to detect transient operations from abnormal operations, and to detect abnormal operations in a coker heater, include collecting on-line process data. The collected on-line process data is generated from a plurality of process variables of the process, or coker heater. A first representation of the operation of the process, or coker heater, is generated based on a first set of the collected on-line process data generated from a first set of the process variables. The first representation is adapted to be executed to generate a first result. A second representation of the operation of the process, or coker heater, is generated based on the first result and based on a second set of the collected on-line process data generated from a second set of the process variables. The second representation is adapted to be executed to generate a prediction of data generated from the second set of the process variables.

Abstract: Methods and systems to detect abnormal operations in a process of a process plant include collecting on-line process data. The collected on-line process data is generated from a plurality of dependent and independent process variables of the process, such as a coker heater. A plurality of multivariate statistical models of the operation of the process are generated using corresponding sets of the process data. Each model is a measure of the operation of the process when the process is on-line at different times, and at least one model is a measure of the operation of the process when the process is on-line and operating normally. The models are executed to generate outputs corresponding to loading value metrics of a corresponding dependent process variable, and the loading value metrics are utilized to detect abnormal operations of the process.