Computer Aided Production - Process manufacturers are under constant pressure to optimize their operations and meet customer demand. Along with market volatility, refining, chemicals and petrochemicals companies are also creaking under the encumbrance of overcapacities and the threat of shrinking margins. With the underlying economic driver to improve plant performance and overcome strong competition, the ability to precisely control the plant and optimize the operation has always been an important priority. With this in mind, many companies have turned to software technology to help address these perennial challenges.

Too often companies experience quality variations, inconsistent throughput and high energy costs, which can cause operations to not run to their actual limit. A combination of unreliable operability and manual processes contribute to this problem. A change of strategy can help maintain optimum quality across a range of products and operating conditions.

Instead of operators manually adjusting control units for specific variables in the plant, advanced systems provide models that automate regulatory and constraint control, as well as deliver process optimization. Hence, Advanced Process Control (APC) is a general term used to describe different types of process control tools and methodologies frequently used for solving multivariable control or discrete control problems. It helps improve the operation of production processes, resulting in the continuous management and optimization of complex process interactions. A dynamic, multivariable interaction model is designed with empirical control software to predict the future path of the process; compare the information to operating constraints and implement an efficient transition to the optimum operating point. The resulting benefits of APC are optimum profits, increased capacity and improved operational performance. Studies have shown significant annual operating cost savings with payback in less than six months.

Steps to Identify the Opportunity

APC is about real-time data processing to positively impact the overall optimization of the production process in order to operate in the most profitable way. The project starts by identifying the business opportunity and developing a plan to deploy the multivariable predictive control within the operation. Once the business feasibility study has been carried out, a pre-test is conducted to ensure the process is understood by all stakeholders. A functional design specification outlines what the controller will do, what process parameters APC will manipulate and where the process will generate the highest profit from the plant.

The next step is to collect data from the process in order to build the process model. The software vendor will plan step-testing typically over two visits - the first being the pre-test and time will be spent in the control room to ensure all relevant regulatory controllers (PID controllers) are tuned as required. Once laboratory data has been collected appropriately and verified, the process engineers will run a series of single variable step-tests (setpoint changes) one at a time, observe how the process responds and record electronically the process changes that have been made to help optimize the specific area of the operation.

This data will allow the engineers to build a preliminary model of the process. Simulations will be carried out and the software vendor will know what further tests need to be conducted to finish the proper step-testing phase. At the same time, the functional design will be edited once more towards its final state. Step-testing can now be quickly completed without causing production loss or process interruptions. Recent innovations in step-testing technology also allow data to be generated in a non-invasive manner where an intelligent controller is implemented using the models generated from the pre-test data (realizing immediate benefits), which then balances robust closed loop control against making setpoint moves that will generate data with suitable characteristics for model identification. This unique capability saves time and does not cause costly disturbances to the operation. Modern software tools know all the interactions in the process and use the preliminary process model to process simultaneous calculations, elicit information and refine the model with the minimum interruptions to the plant, while at the same time achieving constraint control of the process. This process can uniquely reduce the implementation timeline from months to weeks helping to manage the efficiency and time constraints set by the customer.

Breakthrough Flexibility

The process industries encompass an incredible range of products, processes and plant configurations. Therefore, a range of APC solutions are needed to address such a diverse set of production processes. Many companies have utilized aspenONE Advanced Process Control software, which provides several approaches for advanced control applications.

Multivariable model-predictive controllers maintain processes at their optimal operating point. They interface to processes directly with the distributed control system (DCS) or indirectly through process information management systems, capable of safely pushing processes to multiple constraints simultaneously. The APC software efficiently scales to the largest control problem size and can be successfully applied to virtually every linear control problem in refining, chemicals and petrochemicals processing.

Adaptive software modeling provides functionality that incorporates the non-aggressive step-testing while preserving the economic benefits of applications. The software automatically slices out bad data resulting from problematic PID loop conditions, including valve saturation, PID mode changes, process upsets and bad measurements. This improves control model fidelity and the synergy between multivariable predictive control, intelligent data selection and closed loop model identification.

This makes it easier to maintain highly-accurate models as plants change over time.
Control platform software is a consolidated, user-friendly environment that provides a new level of flexibility, efficiency and ease-of-use for engineers. It allows engineers to build, deploy and manage APC solutions for a broad range of processes from simple linear to non-linear. The software provides a complete set of features for loop and controller monitoring, process and product performance management and sustained value. The results are improved quality, reduced energy consumption, increased throughput, higher product yields and a lower cost of ownership.
Prediction and inferential measurements are essential for operators today.

These software applications are powerful for the modeling of inferred product qualities. Inferentials are a supplement for infrequently measured qualities or critical sensors and are also used to support environmental compliance. APC provides a rich choice of model types, making it possible to implement linear or non-linear inferential sensors on-line. Flexible analyzer and laboratory modules automatically adjust the inferred qualities to ensure accuracy. APC enables the use of on-line simulation based on both empirical and rigorous models. Typical applications include improving controller models, generating shadow targets for operators, providing what-if simulations to aid operators and engineers in identifying and resolving process problems and generating KPIs for real-time performance management.

Leading By Example

Today, APC can significantly help manufacturers to lead by example and reach the limits of the operation by optimizing performance, whilst also squeezing out the last drop of profit from the business. This real-time technology and implementation will handle complex changes to all process parameters in a way that the operation is aligned to realize the benefits of operational excellence. The adoption of APC software tools will keep manufacturers compliant with industry regulations and ensure the business is sustainable to remain competitive in a dynamic marketplace.