Emerson Process Experts

ModelingAndControl.com's Greg McMillan and Solutia's Mark Sowell will be presenting at the upcoming ISA 54th International Instrumentation Symposium. Their paper, Advances in pH Modeling and Control, describes the use of embedded simulation, coined "Virtual Plant" and model predictive control to improve the control of pH levels in a plant waste water treatment application.

The authors begin by describing the challenge of pH control:

The pH electrode offers by far the greatest sensitivity and rangeability of any industrial process measurement in terms of the measurement of concentration (hydrogen ions). To realize the full potential of this opportunity requires extraordinary performance of mixing equipment, control valves, reagent delivery systems, flow meters, control system design, and controller tuning.

The virtual plant is described:

A virtual plant can be used to sort out fact from fiction important for insuring performance and reducing capital and operating costs. The virtual plant consists of a download of the actual control system configurations and displays, embedded advanced control tools, and a dynamic process model running on personal computer...

The articles details the control strategy used:

We developed and prototyped model predictive controllers (MPC) to replace the fuzzy logic control system. MPC-1 adjusted the 1st stage pH set point to keep the second stage reagent valve at a minimum position for good response and reliability. MPC-2 trimmed the 2nd stage set point to keep the pH in the tank at an optimum pH.

The authors describe the interaction of the virtual plant with the real plant. They write:

In order to study and improve performance of the control system and the fidelity of the process model for actual process conditions, we put the virtual plant in a read-only mode online running real time. A simple interface module was configured that used object link[ing and] embedding for process control (OPC) to read indicated waste flows, controller set points, and controller modes from the actual plant.

If you are battling pH control in a waste water treatment application, you'll want to give this paper a read. You might also want to get your hands on one of Greg's books, Advanced pH Measurement and Control, if pH control is currently vexing you.

Update: Greg wrote me that the presentation went well and the room could have been bigger to hold all the folks interested in hearing about this topic. He has done a slight revision on page 1 to better tie in the results to the general situation with pH systems. This version is now posted on the original hyperlink above.

Tags: pH modeling | pH control | virtual plant | process simulation | waste water treatment |

April 21, 2008 in Control Strategies, in Process Optimization, in Regulatory Compliance, in pH Control | Comments (0)

Interphex2008, the Pharmaceutical and Biotech manufacturing conference is going on this week in Philadelphia. Before Emerson's Terry Blevins and Mike Boudreau left, they passed along the presentation they are giving on Thursday, March 27. It's entitled, Application of PAT in Product Development. They are joined by University of Texas at Austin PhD graduate student, Yang Zhang and Broadley-James' Trish Benton. Here's an excerpt from the abstract:

The Process Analytical Technology, PAT, initiative encourages innovation in pharmaceutical development, manufacturing, and quality assurance to enhance understanding and control of the manufacturing process. The challenge for many manufactures is to identify how best to address the opportunities that PAT offers. Broadley James, Emerson Process Management, and the University of Texas are working together to examine and quantify the potential to reduce cycle time and out-of-spec product through the use of high fidelity, dynamic simulation and multivariate analytics. The objective of this work is to show that the impact of PAT can be maximized through the integration of these tools during product development (PD).

In the presentation, Terry begins by discussing the U.S. Food and Drug Administrations' PAT initiative, which has a framework that identifies some of the tools they discuss in the presentation. These include:

• Multivariate data acquisition and analysis tools
• Process and endpoint monitoring and control tools
• Continuous improvement and knowledge management tools

Terry describes on-line process analytics including fault detection and quality parameter prediction. Tools for detection of abnormal operations vary for measured and unmeasured disturbances. For measured disturbances, principal component analysis (PCA) captures contributions that can be associated with process measurements. Deviations may be quantified using Hotelling's T-square statistic.

The residual space that is not captured by the principal component score space reflects changes in unmeasured disturbances that can impact operations. These deviations can be measured with the Q statistic, squared prediction error (SPE).

For the quality parameter estimation, detection of deviations is addressed using projection to latent structures (PLS).

Armed with these statistical tools, Mike shows how the basis for bioreactor process modeling. In the book Mike coauthored with Greg McMillan, New Directions in Bioprocess Modeling and Control, they present a first principal bacterial model that was developed for fungal, bacterial, and mammalian cell processes. The intent of the process model is to more quickly evaluate input step techniques and control strategies in the PD stage.

The BioProcess International magazine article, PAT Tools for Accelerated Process Development and Improvement describes this collaborative effort between Emerson and Broadley-James technologists and University of Texas researchers along with how these tools can accelerate life science manufacturers' PD phase.

Tags: bioprocess modeling | Interphex | process analytical technology | PAT | multivariate analytics | first principal model |

March 25, 2008 in Analyzers, in Control Strategies, in Life Sciences, in Simulation | Comments (0)

Over the past several weeks, ModelingAndControl.com's Greg McMillan has shared a three-part series on common control myths with his readers. In the initial post in the series, Common Control Myths – Part 1, Greg offers five myths, which he then shoots down, one by one.

Three of his five involve disturbances in the process. On unmeasured disturbances, Greg offers the following myth and rebuttal:

Unmeasured disturbances are a side issue – if there were no unmeasured disturbances, control would be a non issue because you could home in on the controller output that corresponds to the desired set point for a process variable. You would just need to run some data fitting algorithm one time and the loop would be set for the life of the process. In reality, there are always unmeasured disturbances.

Often the best-laid designs on newly commissioned loops require adjustments over time as the control engineer learns about the unmeasured disturbances impacting the loop.

In the second post in the series, Greg gets deep into the equations to show the scan time effects on peak and integrated errors. Since I've forgotten more than I've remembered, I'll trust him that the math checks out… J

In the final post of the trilogy, Greg debunks control myths six through ten. These include control valve performance, pH sensors, and thermocouples versus RTDs. For example, on pH sensors, he writes:

The most accurate type of pH sensors are used most often – the most popular sensors are the ones that require the least amount of maintenance, such as references with solid electrolytes, even though these may require more time to equilibrate and have a more variable junction potential. The flowing liquid junction reference for the right materials of construction and electrolyte is generally the most accurate but the least used type of pH electrode in industry because of the need to pressurize and refill the reservoir.

If you're early in your career as an automation engineer, you definitely want to subscribe to the ModelingAndControl.com RSS feed as one of your shortcuts to rapid learning.

Update: I was a bit too hasty calling it a trilogy! Greg has unleashed Part 4 of his common control myths.

Tags: controller tuning | loop performance | process control | process dynamics | unmeasured disturbances |

March 14, 2008 in Control Strategies, in Education | Comments (0)

I received an email from Anand Iyer. He's a certified project management professional (PMP) and a project manager in Emerson's engineering center in Pune, India. His project experience covers the gamut from pharmaceuticals, bulk drugs and intermediates to oil, gas and petrochemicals.

He's sent me a paper he's written entitled, Collaborative Measurement Control System Engineering. It describes how measurements close to one another in the process can collaborate with one another to verify their operation. He describes an example around a distillation column:

Now let us take two temperatures (bottom temperatures) in a distillation column and a level measurement. When the level is normal, the two temperatures are same or have a fixed relationship between them. TI1 is placed at a lower level in the column (near bottom) and TC2 is at a higher level (and used for Temp. control). Now TC2 is generally used for control. We can safely say that if Level is normal, and TC2 is under maintenance, TI1 can be used for control (with a minor adjustment to Setpoint if required). Thus Level and Thermocouple TI1 put together can "collaborate" the measurement of Temperature-measurement TC2.

Anand contrasts the traditional approach to a failure with how collaborative measurement strategies can be used in control strategies to avoid outages or process disturbances. In the traditional approach:

…the first thing done if an element were to fail was to swap the elements (either during the shutdown caused by the failure) or by a planned outage or having the loop in manual and doing the swap. At times, we have also used our ingenuity and just swapped the wires at the analog inputs and tuned control setpoints to have the plant up and running in a very short time. And hopefully, in all that chaos, someone had the presence of mind to record the swap on the wiring diagrams.

Using a collaborative measurement strategy:

…says that if level is not low and TC2 is not available then TI1 can be a valid measurement. We alarm the operator that TC2 is not available, fine tune the setpoint if required… All this occurs automatically and there is no outage or disturbance that could result in quality issues.

He extends the thought to Foundation fieldbus devices where the final control elements themselves can perform the logical evaluations and select the available primary or collaborated measurement, increasing the overall robustness of the control strategy. Anand also extends his thinking to wireless devices and how they could be used in a collaborative measurement environment—not as a primary measurement, but as a collaborative measurement to check on other devices nearby.

I hope you'll give Anand's paper a read and add your thoughts.

Tags: collaborative measurement | project management professional | PMP | Foundation fieldbus | distillation column | measurement strategy | control strategy |

March 12, 2008 in Control Strategies, in Distillation Column, in Foundation Fieldbus, in Measurement, in Project Services, in Wireless | Comments (0)

The Automation.com list server has an interesting thread, Three Element Drum Level Control Problem. The question asked was:

We have a waste heat recovery boiler that is supplied by exhaust of a 20MW Gas Turbine. We've seen that at lower turbine loads (75% and below) the three element drum level controller cannot maintain the drum level at desired setpoint. As soon as the load on the Gas Turbine is increased to more than 75% of rated load, the stability keeps getting better. At rated load (20MW) the drum level is very stable and close to the setpoint.

There have been several responses discussing the tuning at various loads. I asked around to see what advice we might have to offer. Emerson's Jack Tippett, a variability management consultant noted that it is critical to know your process dynamics. His point:

If you don't know the process dynamics, control tuning is an art not a science and good control performance is an accident not a certainty.

Once you know your process dynamics, it is important to design your strategy to assist in achieving the process objectives in light of those dynamics. Jack noted a similar situation from his past where he tuned the levels in a 450-megawatt heat recovery steam generator (HRSG) system.

There were six boilers including two lines with high, medium and low-pressure drums. This power producer was unable to achieve a station ramp rate of 25 MW per minute necessary for automatic generation control (AGC) due to serious swings in the drum levels.

After measuring and determining the process dynamics, the process was re-tuned and they were able to achieve the ramp rate and achieve good level control at less than 70% load.

Jack also noted that they chose a single-element control strategy for the following reasons:

1. Feedwater flow control requires a working flow meter: the sense lines for the flow transmitter were outside and were subject to freezing. The Fisher valve had a DVC positioner and AMS software to monitor incipient valve non-linearities (which are the main reason for the second element.)
2. The open loop dynamics (changing the feedwater valve position manually and watching the response to level) on all six boilers showed very small dead times (1 to 6 seconds). This meant that the proportional-integral (PI) level tuning could be very aggressive. As a result, there was no value in the third element (steam flow feed forward)—the level control could be fast enough to respond the changes in level due to steam demand changes. The real need for the feed forward from steam is when the level dynamics are very slow (30 – 90 seconds dead time) so that the feedwater flow can anticipate the long-term level changes (due to steam demand) in spite of the shrink/swell effect.

By having good measurement in the flow, valve position, and valve characteristics and good understanding of the process dynamics across its operating range, Jack and the plant engineers were able to successfully implement a simple single-element control strategy.

Tags: drum level control | process dynamics | heat recovery steam generator | HRSG | automatic generation control | AGC | single element control |

January 14, 2008 in Boilers, in Control Strategies, in Energy Management, in Variability Management | Comments (1)

I came across an email that the ISA Honors & Awards Committee has selected the paper, Improving PID Control with Unreliable Communications, for its excellence in documentation award. Emerson's Deji Chen, Mark Nixon, Terry Blevins, Willy Wojsznis and the University of Texas, Department of Computer Sciences' Jianping Song and Aloysius K. Mok wrote the paper.

The paper examines PID control in a wireless network where intermittent loss of communications is likely to happen. It identifies the poor dynamic response of standard PID algorithms in this loss of communications scenario. The team proposed an enhanced PID algorithm to improve dynamic response under these conditions.

Terry Blevins summarized the paper well in an earlier post on the Modeling and Control blog. The post, PID Modifications for Unreliable Communications describes the situation:

One of the technical challenges is that the 2.4 GHz spectrum defined by IEEE 802.15.4 is also used by Wi-Fi and Bluetooth devices. Also, some electrical devices found in industry generate noise in this frequency band. Thus, at times it is expected that a transmission will be corrupted. To help minimize the impact of these other devices on communications, the Time Synchronized Mesh Protocol (TSMP) selected for wireless HART uses frequency hopping. Even so, at times it is expected that multiple transmissions of a measurement used in control or multiple communications of control actions to an actuator may be lost.

Terry describes how the loss of communications can cause the PID loop to continue executing and wind up due to the reset action. This reset action can be disruptive to the control of the loop. And, if the derivative (the D in PID) action is used, the loss and resumption of the control measurement signal can cause a spike, again bumping the control of the loop.

The Emerson and UT technologists worked through a solution to minimize the impact of this loss of communications. Terry sums up the change:

However, by modifying the reset and derivative calculation to account for the time since the last measurement update, then it is possible to minimize the impact of loosing multiple measurement transmissions.

If you want to look at the math behind this innovation, check out the overview presentation, PID for Unreliable Communications, given at ISA 2006.

Congratulations to the team for their contribution to furthering the advancement of wireless technologies in process automation!

Tags: wireless | PID | reset action | derivative action | IEEE 802.15.4 |

July 17, 2007 in Control Strategies, in Interoperability, in Wireless | Comments (0)

In our continuing series of screencasts, I'm trying to give examples of how advanced diagnostics in Foundation fieldbus devices can be used in control strategies to avoid abnormal situations and potential losses in production.

Emerson's Rune Reppenhagen shows in this quick 2 minute, 47 second screencast, how an advanced model predictive control strategy in a DeltaV controller automatically recognizes a failure diagnostic in a temperature transmitter and switches the mode of control over to a manual state.

At the same time, this diagnostic alerts the operator of the situation, and the AMS Device Manager software shows the condition of the transmitter so it can be quickly repaired.

By using the advanced diagnostics from these intelligent field devices in the control and advanced control strategies, conditions which impact the availability and quality of the process can be avoided.

Tags: Foundation fieldbus | temperature transmitter | model predictive control | MPC | advanced process control | APC | screencast | diagnostics |

May 18, 2007 in Abnormal Situation Prevention, in Control Strategies, in Foundation Fieldbus, in Screencast | Comments (0)

For those of you challenged with the vagaries of pH control, I wanted to make sure you had seen the news of an upcoming pH Control web seminar, arranged by ISA, featuring ModelingAndControl.com's Greg McMillan. The web seminar covers the root causes of poor performance in pH control systems.

In a recent post, Greg describes how he plans to share his experiences:

I spent a lot of time on pH startups. I found most of the key design concepts needed for success where not discussed anywhere, For example, the normal dip tube design for reagent injection is disastrous and the mixing and valve resolution requirements are exceptional. I discovered how I could reduce the number of stages of neutralization, offer inexpensive alternatives to the classical neutralization vessel, and decide when signal characterization could help or hurt your control objectives.

Unlike his recently released free eBook, this May 16 web seminar (2:00pm - 3:30pm Eastern U.S. Time) does have a cost. It's $195 (USD) for ISA members and $225 (USD) for non-members. If you're not the lone person in your organization who struggles with pH control, Greg suggests:

The seminar is much more cost effective if the registrant connects in a conference room with a computer projector.

If you can't make this event, Greg has also published a book on this topic, Advanced pH Measurement and Control, 3rd Edition.

Tags: pH Control | ISA | neutralization | reagent injection |

May 10, 2007 in Control Strategies, in Education, in pH Control | Comments (0)

Recently I discovered in my PAT RSS persistent search feed an article in Pharmaceutical Processing magazine entitled, PAT Solutions-Eight advanced process control technologies worth considering. This article was written by Rick Rys, President, at R2 Controls, and Janice Abel, Director, Global Pharmaceutical and Biotech Industries, at Invensys.

Since ModelingAndControl.com's Greg McMillan recently co-authored a book New Directions in Bioprocess Modeling and Control-Maximizing Process Analytical Technology Benefits and recently had an article published entitled Maximizing PAT Benefits from Bioprocess Modeling and Control in the November 2006 issue of Pharmaceutical Technology IT Innovations, I had to ask for his thoughts.

Greg sent me a great email which I'll pass along with my edits to insert hyperlinks:

The DeltaV systems offers the advanced control technologies mentioned in the PAT article, such as synthetic analyzers, feedforward and predictive control, dead time compensation, and model predictive control in its standard integrated graphical configuration studio that uses Fieldbus function blocks. The synthetic analyzers not only include online regression models such as Neural Networks but also embedded first principal models. Furthermore, innovative analytics, control systems, and models can be prototyped faster than real time in a virtual plant on a desktop or laptop PC anywhere. The virtual plant uses an exact duplicate rather than an emulation or simulation of the control system in the control room. Advanced technologies in the virtual plant can be developed and tested from the high speed play back of historical data from existing systems used to automate bench top fermentors. This includes a new adaptive control technology that identifies process dynamics and indicates the relative improvement possible from better control. The high speed virtual experimentation capability of the virtual plant is a key feature and may be the only way to provide enough historical data particularly on "what if' scenarios since a fermentor batch for most new bioprocesses takes 14-17 days.

The same virtual plant can be used for education of operations and technical support by the dynamic restore and high speed playback of instructive periods of operation.

The technologies can be connected to the bench top system for evaluation, verification, and adaptation of models early on in the commercialization process.

The uses and advantages of the synergistic environment of the virtual plant are explored in the book New Directions in Bioprocess Modeling and Control, the article "Maximizing PAT Benefits from Bioprocess Modeling and Control" in the November 2006 issue of Pharmaceutical Technology IT Innovations, and in the lectures on the Control Insights website. The important practical implications of the extremely slow one direction integrating response of biomass and product concentration on modeling and control are also discussed in the book, article, and website and on the companion Modeling and Control blog site.

The integration and knowledge management of a diversity of technologies in DeltaV addresses the essence of the PAT initiative as expressed in the following statements by the FDA:

Process Analytical Technology:

• It is important to note that the term analytical in PAT is viewed broadly to include chemical, physical, microbiological, mathematical, and risk analysis conducted in an integrated manner.

Process Analytical Technology Tools:

• Multivariate data acquisition and analysis tools
• Process and endpoint monitoring and control tools
• Continuous improvement and knowledge management tools
• An appropriate combination of some, or all, of these tools may be applicable to a single-unit operation, or to an entire manufacturing process and its quality assurance.

Tags: process analytical technology | PAT | FDA | virtual plant | bioprocess | modeling | advanced control |

April 19, 2007 in Control Strategies, in Fermentation, in Life Sciences | Comments (0)

Batch process manufacturers have long understood that applications which require both sequential and continuous control have been a challenge. A typical example is a centrifuge application commonly found in Biotech and Pharmaceutical manufacturing processes. A centrifuge separates solid and liquid material by spinning a sieve-like device at high rate of speed, and recovering the liquid, solid or both materials.

I caught up with Brian Crandall, a Life Sciences industry project engineer. He said that proper control was critical since centrifuges are quite expensive, and sensitive to a variety of failure conditions. These conditions need to be addressed within seconds to prevent equipment damage and possible injury to operations staff.

Brian summed up the control challenge as the centrifuge having various operational states. Moving between these states is best done using sequential operations. However, monitoring of the failure conditions, which change in severity and action depending upon the operational state, must be done in parallel with the sequencing.

If a failure condition occurs, the current sequence has to be stopped, and the specific failure sequence started within a minimum timeframe. The S88 Batch Model defines a sequential state driven approach, but it does not fit the requirements of this application. The big issue is the failure monitor does not offer continuous monitoring required for quick reaction to the failure condition.

Using a DeltaV system for this particular Biotech application, Brian designed, tested and implemented a modified S88 state model that had the ability to stop a sequence without waiting for a transition thus meeting the high-speed timing requirements of the equipment. Multiple sequences would be required for the main sequence, shutdown, and E-Stop to allow stopping one sequence and starting another at the same time. Also the code design needed to modular to fit the rest of the S88 modular design philosophy. Also, this design placed control at correct level in S88 batch model, at the equipment module level.

Some failure conditions the design addressed included: high vibration, VSD fault, low seal water pressure, and low instrument air pressure. Depending on the state of operation, the failure conditions required different actions per a failure condition matrix.

Other industries have applications requiring this mix of continuous and sequential control. Some examples include a refiner in the pulp and paper industry, extruders in the specialty chemicals industry, and other state-driven processes or equipment.

Tags: S88 | ISA-88 | centrifuge control | continuous control | sequential control | control strategy | project execution |

May 26, 2006 in Control Strategies, in Life Sciences, in Project Services | Comments (0) | Trackback (1)