27 real Digitalisation & APC questions from the Plant Operations bank, as asked in Indian campus drives and tech interviews. Every question has a verified answer and an AI-tutor explanation on placd — free to start.
1. What is Advanced process control?
Junior
A.time-series database such as AVEVA PI that compresses and stores tag values for trending, reporting and analytics
B.least-squares adjustment of measured flows to satisfy mass-balance constraints, exposing gross errors in individual meters
C.dynamic process model linked to a replica DCS console for training on start-up, shutdown and upsets without touching the plant
D.multivariable model-based layer above DCS PID loops that pushes a unit against constraints to raise throughput, yield or energy efficiency
2. Which term means: "multivariable model-based layer above DCS PID loops that pushes a unit against constraints to raise throughput, yield or energy efficiency"?
A.Advanced process control — steady-state rigorous model run every few hours to compute optimal set points for APC from current prices and constraints
B.Advanced process control — controller that predicts future outputs from a step-response model and optimises moves over a horizon subject to constraints, as in DMC or RMPCT
C.Advanced process control — live model of the plant synchronised with historian data to predict performance, plan turnarounds and test what-if changes
D.Advanced process control — multivariable model-based layer above DCS PID loops that pushes a unit against constraints to raise throughput, yield or energy efficiency
A.Process historian — steady-state rigorous model run every few hours to compute optimal set points for APC from current prices and constraints
B.Process historian — time-series database such as AVEVA PI that compresses and stores tag values for trending, reporting and analytics
C.Process historian — inferential model estimating a hard-to-measure property such as product purity from temperatures and pressures, corrected by lab results
D.Process historian — controller that predicts future outputs from a step-response model and optimises moves over a horizon subject to constraints, as in DMC or RMPCT
A.Operator training simulator — controller that predicts future outputs from a step-response model and optimises moves over a horizon subject to constraints, as in DMC or RMPCT
B.Operator training simulator — dynamic process model linked to a replica DCS console for training on start-up, shutdown and upsets without touching the plant
C.Operator training simulator — least-squares adjustment of measured flows to satisfy mass-balance constraints, exposing gross errors in individual meters
D.Operator training simulator — live model of the plant synchronised with historian data to predict performance, plan turnarounds and test what-if changes
11. Which term means: "controller that predicts future outputs from a step-response model and optimises moves over a horizon subject to constraints, as in DMC or RMPCT"?
A.Model predictive control — steady-state rigorous model run every few hours to compute optimal set points for APC from current prices and constraints
B.Model predictive control — time-series database such as AVEVA PI that compresses and stores tag values for trending, reporting and analytics
C.Model predictive control — WirelessHART or ISA100 sensors and edge devices adding low-cost monitoring on rotating equipment and steam traps
D.Model predictive control — controller that predicts future outputs from a step-response model and optimises moves over a horizon subject to constraints, as in DMC or RMPCT
14. Which term means: "inferential model estimating a hard-to-measure property such as product purity from temperatures and pressures, corrected by lab results"?
A.Soft sensor — live model of the plant synchronised with historian data to predict performance, plan turnarounds and test what-if changes
B.Soft sensor — WirelessHART or ISA100 sensors and edge devices adding low-cost monitoring on rotating equipment and steam traps
C.Soft sensor — inferential model estimating a hard-to-measure property such as product purity from temperatures and pressures, corrected by lab results
D.Soft sensor — multivariable model-based layer above DCS PID loops that pushes a unit against constraints to raise throughput, yield or energy efficiency
A.Digital twin — controller that predicts future outputs from a step-response model and optimises moves over a horizon subject to constraints, as in DMC or RMPCT
B.Digital twin — least-squares adjustment of measured flows to satisfy mass-balance constraints, exposing gross errors in individual meters
C.Digital twin — live model of the plant synchronised with historian data to predict performance, plan turnarounds and test what-if changes
D.Digital twin — steady-state rigorous model run every few hours to compute optimal set points for APC from current prices and constraints
A.IIoT and wireless instrumentation — inferential model estimating a hard-to-measure property such as product purity from temperatures and pressures, corrected by lab results
B.IIoT and wireless instrumentation — steady-state rigorous model run every few hours to compute optimal set points for APC from current prices and constraints
C.IIoT and wireless instrumentation — controller that predicts future outputs from a step-response model and optimises moves over a horizon subject to constraints, as in DMC or RMPCT
D.IIoT and wireless instrumentation — WirelessHART or ISA100 sensors and edge devices adding low-cost monitoring on rotating equipment and steam traps
A.Data reconciliation — dynamic process model linked to a replica DCS console for training on start-up, shutdown and upsets without touching the plant
B.Data reconciliation — inferential model estimating a hard-to-measure property such as product purity from temperatures and pressures, corrected by lab results
C.Data reconciliation — least-squares adjustment of measured flows to satisfy mass-balance constraints, exposing gross errors in individual meters
D.Data reconciliation — controller that predicts future outputs from a step-response model and optimises moves over a horizon subject to constraints, as in DMC or RMPCT
A.Real-time optimisation — inferential model estimating a hard-to-measure property such as product purity from temperatures and pressures, corrected by lab results
B.Real-time optimisation — controller that predicts future outputs from a step-response model and optimises moves over a horizon subject to constraints, as in DMC or RMPCT
C.Real-time optimisation — time-series database such as AVEVA PI that compresses and stores tag values for trending, reporting and analytics
D.Real-time optimisation — steady-state rigorous model run every few hours to compute optimal set points for APC from current prices and constraints
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