(SEM VIII) THEORY EXAMINATION 2022-23 COMPUTERIZED PROCESS CONTROL

COMPUTERIZED PROCESS CONTROL (KOE-092)

B.Tech Semester VIII – Theory

SECTION A

(a) Role of computers in process control

Computers play a central role in modern process control systems by enabling accurate monitoring, control, and optimization of industrial processes. They continuously collect real-time data from sensors, process this information using control algorithms, and generate appropriate control signals for actuators. Computers also perform supervisory functions such as alarm handling, fault diagnosis, data logging, and performance analysis. By replacing manual control, computers improve reliability, reduce human error, enhance productivity, and ensure safe operation of complex industrial plants.

(b) Communication networking in process control

Communication networking in process control refers to the structured exchange of information among sensors, controllers, computers, and actuators within a control system. It allows real-time transmission of measurement data, control commands, and status information. Industrial communication networks ensure coordinated operation of distributed control components, improve system response time, and support centralized monitoring. Reliable networking is essential for maintaining accuracy, synchronization, and stability in automated control systems.

(c) Elements of a computer-aided process control system

A computer-aided process control system consists of several interconnected elements that work together to regulate a process. Sensors measure physical variables such as temperature, pressure, and flow. Signal conditioning units convert sensor outputs into suitable electrical signals. Controllers compute control actions based on process data and desired setpoints. Actuators implement control actions on the process. The computer system supervises all activities, provides data storage, and enables human-machine interaction through interfaces.

(d) ISO reference model for communication

The ISO reference model, also known as the OSI model, is a standard framework that defines how data communication occurs in a network. It consists of seven layers: Physical, Data Link, Network, Transport, Session, Presentation, and Application. Each layer performs a specific function, ensuring structured communication, interoperability, and reliability in control system networks.

(e) Need of modelling of the system

System modelling is necessary to understand and predict the behavior of a process under various operating conditions. A model provides a mathematical or logical representation of the system, which is essential for controller design, simulation, stability analysis, and optimization. Modelling helps engineers evaluate system performance without disturbing actual operations and supports effective decision-making.

(f) Define goal

A goal in process control represents the desired objective that the system aims to achieve. It may include maintaining process variables at specified values, minimizing deviations, improving efficiency, reducing energy consumption, or enhancing product quality. All control strategies are designed to achieve these predefined goals.

(g) Applications of statistical control

Statistical control is applied in manufacturing and process industries to monitor quality and maintain consistency. It helps detect process variations, identify abnormal conditions, and reduce defects. Statistical control techniques improve productivity, ensure uniform product quality, and support continuous process improvement.

(h) Advantages of computerized process control

Computerized process control offers high accuracy, fast response, improved reliability, and better process optimization. It reduces dependency on human operators, enables continuous monitoring, improves safety, and supports advanced control strategies. These advantages lead to increased efficiency and reduced operational costs.

(i) Challenges in electric oven temperature control

Electric oven temperature control faces challenges such as thermal lag, uneven heat distribution, environmental disturbances, and sensor inaccuracies. Opening the oven door and variations in load also affect temperature stability. Accurate modelling and proper controller tuning are required to overcome these challenges.

(j) Limitations of cascade control

Cascade control increases system complexity because it involves multiple controllers and feedback loops. Improper tuning of inner or outer loops can cause instability. It also requires additional sensors and maintenance, which increases system cost and design effort.

SECTION B

2(a) Classification of computer-aided process control system

Computer-aided process control systems are classified based on their level of automation and functionality. Supervisory control systems monitor processes and provide optimization without directly controlling actuators. Direct digital control systems replace analog controllers by performing real-time control through computers. Distributed control systems divide control functions among multiple controllers to improve reliability and scalability. Hierarchical control systems organize control tasks at different levels to efficiently manage large-scale industrial processes.

2(b) Data transfer techniques

Data transfer techniques define how information is exchanged between components of a control system. Parallel data transfer sends multiple bits simultaneously, offering high speed but requiring more hardware. Serial data transfer transmits data bit by bit and is widely used due to its simplicity and cost-effectiveness. Synchronous transfer relies on a common clock signal, while asynchronous transfer uses start and stop bits. These techniques ensure reliable communication in control applications.

2(c) Process model and physical model

A process model is a mathematical representation that describes the dynamic behavior of a system using equations. It is mainly used for simulation, controller design, and performance analysis. A physical model, on the other hand, is a scaled-down or simplified physical representation of the actual system. Physical models help visualize system behavior, while process models are essential for analytical and computational purposes.

2(d) Predictive control and adaptive control

Predictive control uses a mathematical model to predict future system behavior and calculate optimal control actions in advance. It improves performance by considering constraints and future disturbances. Adaptive control automatically adjusts controller parameters in response to changes in process characteristics. Both techniques are widely used in systems with varying dynamics and uncertainties.

2(e) Reheat furnace temperature control

Reheat furnace temperature control is critical in metal processing industries to ensure uniform heating of materials. Sensors measure furnace temperature, controllers compare it with desired values, and actuators regulate fuel or power input. Computerized control improves accuracy, energy efficiency, and product quality by continuously adjusting operating conditions.

SECTION C

3(a) Architecture of computer-aided process control system

The architecture of a computer-aided process control system consists of field devices, control units, communication networks, supervisory computers, and human-machine interfaces. Sensors collect process data, controllers compute control actions, and computers provide monitoring, optimization, and data management. This structured architecture ensures reliability, flexibility, and scalability.

3(b) Process-related interfaces in control systems

Process-related interfaces connect physical processes with digital control systems. These include analog-to-digital converters, digital-to-analog converters, input/output modules, and communication interfaces. They enable accurate data acquisition and reliable control signal transmission, ensuring smooth interaction between hardware and software components.