Incatools blog

PID control is widely used for process control across various industries. However, PID control performs well if the process behaviour is almost linear, which is not the case most of the time. Non-linearities are quite common in practice, although they are often not properly identified or considered for the design of the control system.

Cross-limiting Control is implemented, what’s next?

From refineries to petrochemical and chemical industries, the Smith predictor is used to improve the control of processes with a long dead time. The delayed behavior of the process is compensated by using an approximated model in the control structure. It is common for process control engineers to evaluate the need for such control structures and keep them performant. This blog explains the consequences of a long process dead time and what tools to use when tuning your Smith Predictor. 

Once installed, INCA MPC has proven to generate sustained benefits for operating companies. To configure INCA MPC, you need to take several steps: choose the targets and constraints, determine the controller structure, set up the communication between the controller and the process, and tune the controller… After the first version of the controller, updates will be needed later. These updates range from tweaking a tuning parameter to adding extra variables to the controller and updating the targets. All these steps must be executed carefully. The iterations involved in finding mistakes slow down the project’s progress. 

An earlier blog described how INCA Architect makes configuring an INCA MPC application easy by automating tasks, grouping related information and continuously monitoring the consistency and validity of the configuration.

But what if your situation is non-standard? Can you also use the INCA tools in that case? Can you verify the correct behavior of your configuration before going online? This blog discusses how the combination of INCA MPC and INCA Architect gives you all the tools to tackle these cases too!

Non-minimum phase systems are difficult to understand and even difficult to tune manually. The understanding of non-minimum phase system and control strategies explain in this blog.

The response of a non-minimum phase system to a step input has an "undershoot". This means, if the output was initially zero and the steady-state output is positive, the output becomes negative before changing direction and converging to its positive steady-state value.These systemsare difficult to control due towaiting time until the initial undershoot for a positive gain system or overshoot for a negative gain system.

Hydrogen is one of the key raw materials used in the refinery. It is used in various process units: Hydrocracker, Aromatic plants, to name a few. Since the process is dynamic, the demand for H2 keeps on varying, and excess is vented out either to fuel gas or to flare to control the Header pressure. The good news is that the venting of the H2 can be reduced. In some cases, it can be reduced to close to zero.

Many operations managers face a challenge to maintain all PID loops on their plant in good working order. Plant safety may be at stake in some cases if some of these PID loops are kept in manual mode. Poor PID tuning may also contribute towards degradation in plant performance, leading to unstable operation and, eventually, reduced profitability. Operations managers need to create a culture of continuous PID performance monitoring and improvement, ensuring safe and optimal plant performance. How do you embark on this journey, and what are the best practices?

Large processing facilities such as Refineries and Chemical plants spend money on technology to enhance process performance. Metrics are typically put in place to measure how well the technology is performing, and control engineers are required to report on these metrics regularly. There are thousands of PID loops on a typical refinery; what strategy should you use to identify poor performers and then rectify the issues?
How do you ensure that the reported metrics provide your management a full and clear picture of PID loop performance?

A split range control strategy uses software or hardware-based splitter and two or more final control elements to control a process variable. Split range strategies can be found in different applications across process industries: temperature control with heating/cooling media, large and small valves installed in parallel, pressure control with a vent valve, etc. 

Usually, split range applications are tricky and time-consuming, and you should know what I’m talking about as a process control engineer

Oscillations over the entire range of operation and across the split point, typically improperly selected at 50%, are common in real operation. 

In this blog, we will discuss the usual problems you might face when configuring and tuning a split range strategy and the possible and practical solutions to overcome it.