A modern plant can have high-specification instruments, a capable control system, and a data historian holding thousands of tags—and still operate far below what its equipment can actually deliver. The problem is rarely one failed device. More often, performance is lost across the chain: sensor location, installation effects, calibration, signal conditioning, controller tuning, final-control-element mechanics, data quality, and operating decisions. The result is extra operating margin, slower transitions, and longer product holds than the process requires.
Process Instrumentation & Control treats the production system as one continuous engineering chain, from process physics to measurement, information, decision, action, and verification. Across twenty-two chapters and more than five hundred illustrated pages, it shows how information is created, transformed, and sometimes degraded at every interface—and how to diagnose the system as a whole instead of treating each discipline in isolation.
Inside this handbook, you will learn how to:
- Trace a weak control loop to its real cause by separating sensor, installation, signal-conditioning, tuning, and final-element problems.
- Judge whether a measurement deserves trust using accuracy, precision, uncertainty, calibration, and traceability as practical engineering tools.
- Work confidently with pressure and differential pressure, flow, level and interface, temperature, and analytical measurement.
- Specify and diagnose valves, actuators, variable-speed drives, instrument wiring, power, grounding, and hazardous-area interfaces.
- Perform calibration, commissioning, and loop checking as evidence-producing disciplines while maintaining recoverable plant documentation.
- Tune PID loops with process dynamics in mind and apply cascade, feedforward, ratio, multivariable, constraint, inferential, and model-predictive control where appropriate.
- Integrate functional safety, alarm management, and industrial cybersecurity across the plant lifecycle.
- Apply soft sensors, digital twins, machine learning, predictive maintenance, and edge computing only when the underlying measurements and data context can support them.
Key topics include measurement science and uncertainty; sensors, transducers, and installation effects; pressure, flow, level, temperature, and analytical measurement; valves, actuators, and drives; signals, wiring, grounding, and hazardous-area practices; P&IDs, tags, loop diagrams, datasheets, and instrument indexes; calibration, commissioning, and asset management; process dynamics and advanced control; PLC, DCS, and supervisory architectures; industrial communications and interoperability; functional safety; alarm management; cybersecurity; digital twins; soft sensors; analytics; machine learning; predictive maintenance; brownfield modernisation; troubleshooting; and staged smart-plant implementation.
Each chapter begins with a realistic operating problem—a startup that will not settle, a night-shift alarm on a recovery vessel, or a compressor trip that floods the operator with consequential alarms—then reconstructs the engineering that explains it.
Written for instrumentation and control engineers, process, chemical, and mechanical engineers, maintenance and reliability technicians, automation and control-system specialists, operations and production staff, plant data teams, and engineering managers, this handbook provides a complete view of the modern plant, one interface at a time.
