CONCEPTUAL
PROCESS DESIGN

is the use of mathematical and computational methods to simulate and analyze the behavior of chemical systems. It is an important tool in the design, optimization, and control of chemical processes. A chemical process design can be modeled in steady-state and/or dynamic conditions.

An example project link is given at the bottom of the page.

Steady-State Models

are based on the assumption that the process is at a steady state,

where the system's properties do not change over time.

This type of model is used to predict the steady-state behavior of a process,

such as the composition and flow rate of the process streams and energy flows.

These models can be represented mathematically

by using mass and energy balance equations.

Dynamic Models

take into account the time-dependent behavior of a process.

They are used to predict the dynamic behavior of a process,

such as how the process responds to changes in process conditions.

These models can be represented mathematically

by using differential equations or difference equations.

Process design

Process models are generally used to predict the performance of a process under different operating conditions,

and to optimize the process design. The design can be new processes or for modifying existing processes.

Process models can be used to evaluate the feasibility of a proposed process, predict the performance of the process under different conditions, and optimize the process to meet specific performance targets.

This includes optimizing process variables such as temperature, pressure, flow rate, and composition.

Process models can also be used to evaluate the impact of different design options on the process performance,

such as using different equipment or process configurations, which would ultimately lead to a more efficient and

cost-effective process design, resulting in savings for the company.

Process optimization

Process models can be used to optimize the process performance, such as determining

the optimal operating conditions for a process. This can be done by simulating the process under different conditions

and identifying the operating conditions that result in the best performance which include

maximizing production, minimizing costs, or improving product quality.

Process models can also be used to identify the key process variables that have the greatest impact on

process performance, and to develop strategies for optimizing these variables. This can be done through optimization algorithms such as gradient descent, evolutionary algorithms and linear programming.

The use of process models in optimization can lead to improved process performance, increased production, improved product quality and reduced costs, which ultimately benefits the company by increasing profits and competitiveness.

Process
troubleshooting

Process models can be used to identify the root cause of process problems, such as identifying

the source of a process upset.This can be done by simulating the process under different conditions and

comparing the results with the actual process data.

Process models can also be used to simulate the process response to different disturbances, such as changes in feed composition or process conditions, and to identify the key process variables that are affected by the disturbance.

Once the root cause of the problem is identified, process models can be used to develop a strategy for addressing

the problem and improving the process performance. This can lead to improved process performance, reduction in downtime and maintenance costs, and ultimately increase in the production of the company.

Process control

Process models can be used to develop control strategies for a process, such as determining the setpoints for

process variables and designing control loops. These models can be used to develop control algorithms that can be implemented in a control system, such as a programmable logic controller (PLC) or a distributed control system (DCS).

The use of process models in control can improve the performance of the process by

providing a better understanding of the process dynamics, and how to control it. This can lead to

more efficient and stable process operation, by maintaining the process variables within specified limits,

and provide the ability to quickly respond to changes in the process conditions.

Additionally, it can also provide the ability to improve product quality by maintaining the product within

specified limits and improve safety by monitoring and controlling the process conditions.

Overall, chemical process modeling, together with process control, is a powerful tool that allows process engineers to better understand and predict the behavior of chemical processes, and to develop effective control strategies to improve the performance of the process, which ultimately leads to more efficient, stable and safe processes, which is beneficial for companies in terms of cost reduction, increasing production, product quality and safety.

Industry leader conceptual process design, modeling and simulation softwares: