pH Control

ModelingAndControl.com's Greg McMillan was giving his pH Control Solutions webcast a dry run yesterday and it was an opportunity I didn't want miss. I always learn something when I'm around Greg. Greg will be conducting this live ISA web seminar on pH Control tomorrow, March 10, 2010 at 2-3:30pm U.S. Eastern (GMT -5) time. It's not free, so visit the ISA webinar registration page to sign up.

It's a chance to listen to Greg and ask questions about challenges and solutions to your toughest pH Measurement and Control solutions. He derived some of his thoughts from his book Advanced pH Measurement and Control, 3rd Edition.

Those that know Greg know he loves his Top Ten, David Letterman-style lists. In this presentation, he'll share his top ten signs of a rough pH startup. I'll share one of them. You know you've got a rough pH startup when the plant manager leaves the country.

pH poses measurement challenges in sensitivity and rangeability like no other. Normally, an instrument engineer considers a turndown ratio or rangeability of 100 to 1 as quite large. Try 100,000,000,000,000 to 1 for a pH sensor measuring a pH 0 (1.0 hydrogen ion/0.00000000000001 Hydroxyl ion concentration) to pH 14 (0.00000000000001 hydrogen ion/1.0 Hydroxyl ion concentration). It was enough zeros that I was losing count, so I cut and pasted from Greg's presentation!

Another big challenge is the non-linear, s-shaped titration curves (pH versus reagent/influent ratio). If you follow the link for titration curves to the Wikipedia link, you'll see a picture of this non-linear curve. Greg noted that the steep vertical part is deceiving. As you zoom in it's actually another titration curve. As such, it's critical to get numerical values and a sufficient number of data points around the setpoint. Greg describes various titration curve scenarios including weak acid/strong base, weak acid/weak base, multiple weak acids/weak bases, and strong acids/weak bases.

Greg describes how large savings in reagent is possible for the flat parts of the titration curves. pH sensor drift can have a large impact on the reagent calculations and Greg discusses the advantages of doing Feedforward flow control on the ratio of reagent to influent flow. The Feedforward control requires pH feedback correction unless the setpoint is in the flat part of the titration curve. He recommends using Coriolis mass flow meters and having constant influent and reagent concentrations.

He covers much more from the construction and operation of double-junction combination pH electrodes to the need for three pH probes and a mid-select algorithm to handle the natural drift in pH measurements. He offers many pH control strategy examples such as cascade, full throttle batch, linear reagent demand batch to name a few.

If you are fighting pH measurement and control issues at your plant, it may be worth the fee and time to hear Greg and have the opportunity to ask your questions of him directly.

MP3 | iTunes

Update and bump: Greg's article is now live on the ISA.org InTech website. Below is the original Dec. 15, 2009 post.

I received an advanced copy of an article ModelingAndControl.com's Greg McMillan has recently completed, Exceptional Opportunities for Smart Wireless pH and Conductivity Measurements. In the article, he summarizes these opportunities:

...for inferential measurements, solution temperature correction, efficient calibration, noise minimization, and predictive maintenance by taking the advantage of smart features and wireless communication.

On inferential measurement, Greg notes that the connectivity, intelligence and portability of wireless conductivity and pH measurements increase the possibilities for successful inferential measurement creation. He writes:

The availability of the primary process variable (pH), and the auxiliary variables (milliVolts, temperature, reference impedance, glass impedance, and RTD resistance)... for a smart wireless pH transmitter, facilitates the monitoring of sensor performance besides developing relationships for solution temperature compensation, solvent concentration, and CO₂ loading.

Inferential models developed within the automation system neural network algorithms use conductivity, pH, and temperature inputs to better predict solvent concentration and CO₂ loading.

With respect to temperature compensation, Greg observes that the standard temperature compensation in pH measurement as defined by Nernst equation does not account for actual solution pH changes with changing temperature. Additional solution temperature compensation in smart pH transmitters is beneficial for many applications. Greg shares:

Lab tests where the pH and temperature of the sample are varied to cover the operating range are required to quantify the effect of weak acid and base dissociation constants on solution pH. Smart wireless pH transmitters allow the user to develop, document, and integrate the solution temperature compensation results from lab tests.

Most automation engineers have faced issues with electromagnetic interference (EMI) causing noise on their process measurements. For pH measurements, spikes can be caused by ground loops or the operation of motors and variable speed drives. Wiring to the instrumentation can act as an antenna for this noise. Wireless devices avoid the EMI issues that wiring induces.

Many pH applications are difficult, due to electrode coating, plugging, and aging that can occur in days or weeks. Wireless lab and field pH and conductivity measurements in a lab process sample:

...creates interesting opportunities for predictive maintenance on when to clean or replace electrodes.

The technology team envisions how these smart pH and conductivity measurements could be enhanced with:

...a model for a particular sensor and run a simplified principal component analysis (PCA) within the transmitter to detect a failure.

The article shares specific examples of the team's work with the University of Texas and their absorber for CO₂ capture and distillation column for solvent recovery. I'll update this post when I know when and where the article will be published. Until then, I hope this gives a brief sample of some of the innovations occurring on the pH and conductivity measurement and control fronts.

MP3 | iTunes

Wow, what a fast and furious week... I'll close the week by highlighting a recent article by ModelingAndControl.com's Greg McMillan. He coauthors the article, Virtual Plant Provides Real Insights, which appears in the January 2009 edition of Chemical Processing magazine.

Greg's a published author on the topic of pH control and widely known his expertise. Working with Monsanto engineers, they sought a better way to control the pH of a wastewater pit. Maintaining pH between a permissible range of 6 and 9 was a labor-intensive activity. It also required veteran operators. Inexperienced operators, typically working night shifts, would call the plant engineers with a nearly full pit with an out-of-range pH and an imminent pumpout about to happen. Plant engineers typically don't enjoy being awakened to hear this.

The initial solution was to replace the pit with two 40,000-gallon tanks. The issue with this solution was high capital costs and limited plant real estate for the tanks. Plant engineers worked with Greg to see if a better solution could be modeled and developed. The goals were to minimize capital costs, provide reliable operations, and be easy enough to operate, even for less experienced operators.

Using much of the wisdom he freely shares at ModelingAndControl.com, Greg developed a virtual plant running a DeltaV system with embedded simulation on his notebook computer. Virtual plant means it runs both the simulation of the plant and the control system logic. Greg describes the setup:

The virtual plant included a dynamic model of the process with material and charge balances as well as mixing and injection delays, and a dynamic model of the control valves with deadband and resolution limitations. The models were configured and embedded in a distributed control system (DCS) along with the control strategies. The integrated nature of the virtual plant eliminated the need for separate programs, interfaces and emulations. We could develop and test the actual control modules and displays used in the plant.

Working with the lab data history, the team developed titration curve tabular data. They next matched the titration curve of the process model with the laboratory titration curve. They ran the demineralization unit batch sequence for different equipment, injection and automation system designs. The model showed where the biggest causes of upsets to pH level occurred.

They could also do what-if analysis to see if fast inline pH control could catch the disturbances and smooth them out. The result of the modeling and control analysis was that the pH could be controlled with 10,000-gallon tanks instead of the original 40,000-gallon tanks for project capital savings of more than 50%.

The article gives the design details of what process designs, process instrumentation, and control strategies were required to achieve the initial objectives sought.

If you've got a retrofit project ahead, you might consider a modeling and control analysis to see if large capital savings are possible. In today's global economic environment, this could make you a hero.

I also wanted to pass along that Greg was conducting a pH survey for a revision to his pH book:

Help Greg McMillan fine-tune his focus on pH issues by answering a few questions online. Taking part will give you a chance to win a copy of "Advanced pH Measurement and Control" as well as other prizes. When taking the survey, if you don't know the answer to a particular question, just select the 0-1% choice.

MP3 | iTunes

Emerson's Fisher division recently announced a new three-way, temperature-control valve and actuator system. The release highlighted its potential use by process manufacturers:

The new GX 3-way has the ability to accurately control the temperature of water, oils, steam, and other industrial fluids. Applications include heat exchangers and lubricating skids.

For those not well versed with three-way valves, you'll find use for them in both flow mixing (converging) and flow splitting (diverging) applications.

I caught up with Brad Smith, the global GX control valve product manager, about some potential applications for this valve. Brad began by sharing the development objectives for this valve. Typically, when a process manufacturer cannot achieve the required control, they must reassess process-piping arrangements, often going to a 2-valve arrangement. This GX 3-Way valve provides the level of control to avoid re-piping and 2-valve arrangements.

Brad shared with me that the biggest application focus for this 3-way valve is in temperature control around heat exchangers. It was designed for high-capacity applications and precise linear characteristics required for accurate temperature control. Brad cited a specific heat exchanger application in beer brewing where the wort temperature is maintained with a glycol coolant.

Another common application for this 3-way valve is pH control on feedwater to a boiler. When the pH of the feedwater rises beyond a predetermined level, a three-way valve adds fresh make up water to reduce the pH back to target levels.

A third application Brad discussed was for test separator manifolds. Test separators are mainly used in oil & gas production facilities to measure the amounts of oil, gas, and water from the well. The manifold contains three-way valves coming from each wellhead that uses the test separator. Some installations use the three-way valves while others prefer globe valves.

A final application Brad shared was in the steel industry. Rod mills require good temperature water box control.

Most process manufacturers have temperature control applications requiring mixing flow streams or splitting flow streams. This three-way valve might have the flow characteristics and properties your application requires.

MP3 | iTunes

One way to reduce the sheer volume of email from those you work with is to promise to blog them. Most take this as an idle threat, so unfortunately the emails keep flowing. Here's a case where the threat is not idle, and here's the post to prove it.

The original question came in from a process manufacturer to ModelingAndControl.com's Greg McMillan and asked him for a recommended pH probe for low pH material (1-3pH). My hopefully trusty source, the pH entry in Wikipedia, puts that on a scale with gastric acid.

Greg contacted Dave Joseph, a senior industry manager in Emerson's Rosemount Analytical Liquid business. Dave responded:

In my experience, measuring low pH values in the 1-3 range is not very difficult. Although there is a nonlinear effect called "acid error", the primary source of error is junction potential due to the high concentration of H+. This manifests as a pH reading that ramps quickly into the ballpark but may take quite some time (100 secs or more) to get to the final value. It would be common for the reading to drop from 6 to 2.4 and then tick slowly down to 2.0, for instance. A good sensor for that kind of behavior is a more open junction like our PERpH-X design that allows the potential to stabilize quickly. It would also help cut the time necessary for calibration.

A clean ISFET [Ion-sensitive field effect transistor] sensor responds quickly regardless of the temperature, so the FET is an improvement for very low temperature processes (near 0°C) where high glass impedance causes slow response and noisy readings. In practice, most pH measurement issues have to do with the reference side of the sensor, which is subject to coating, plugging, poisoning, and junction potentials. pH applications can involve many different processes and conditions. Practically all of the troublesome measurements (high temperature, caustic (high pH), steam cleaning) for glass electrodes are even more problematic for ISFETs. In a low pH stream with no other concerns, an ISFET would be expected to function as well as a glass electrode, but with no specific advantages.

Greg's follow up question was:

Are there any hydration requirements for an ISFET? My understanding is that a glass electrode depends upon a hydrated gel layer.

Dave responded:

The glass electrode does use a hydrated gel layer to produce a stable potential. An ISFET works more directly and does not need hydration to make the measurement. That means that an ISFET may recover from a dry environment faster than a glass electrode would. However, both types of electrodes require a reference with a silver/silver chloride solution of water, and the presence of water in the process is required for acceptable continuous measurement.

I thought there was some wisdom in the exchange that needed to be set free from the clutches of my email inbox. Then again, let's see if Greg or Dave ever includes me on another email!

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.

Emerson technologist and ModelingAndControl.com blogger, Greg McMillan, coauthors with Solutia's Mark Sowell an article, Virtual Control of Real PH in the November issue of Control magazine. The wonderful thing about Greg's writing is that it seems to always include experienced-based rules of thumb, a lack of sugarcoating the facts, and large amounts of humor.

In this article, the authors waste no time in mentioning why we should be interested in reading the article. Most plants have pH control applications, even if in their waste-treatment areas. These areas usually have environmental compliance issues and for applications like crystallizers, fermenters, reactors, and strippers, pH control is critical.

My example of Greg not sugarcoating the facts is:

While we tend to focus on the configuration of the DCS, achieving the full potential of the pH measurement requires exceptional attention to every aspect of the system design. Deficiencies in the equipment, piping, valves or sensor selection or installation can cause the system to fail miserably.

This advice alone may save someone loads of troubleshooting time by first looking at the field equipment and installation before fiddling with the automation system's configuration and tuning.

He's also very good at simplifying the approach to pH control problem solving by helping the reader form a quick mental picture:

The name of the game with pH is to minimize the loop dead time to minimize the excursion along the highly nonlinear titration curve.

The solution described in the article is to use a virtual plant--a dynamic simulation of the waste-treatment system--based on a first-principle dynamic model of the pH system and control system configuration. These all run in the same PC. Dynamic simulations can be quite complex but here's where Greg's rules of thumb based on his experience come in. The key is to focus on simplification and attention to the details that really matter. An example of a rule of thumb:

For pH modeling for process control of environmental systems, about 20 acids and bases cover about 90% of the applications. The physical properties requirements are much less (just molecular weight, density and dissociation constants of each acid and base). The waste treatment systems are normally dilute enough so that activity coefficients are not needed.

They used the virtual plant to see if the existing fuzzy logic control could be replaced with a straightforward model predictive control (MPC) strategy. You'll have to read the article to see the full approach but the bottom line was that:

The MPC did a much better than expected job of chasing the acid concentration... We confirmed later that the production unit that was the source of most of the strong acid was having issues. A comparison of the virtual plant and actual plant control valve positions and pH response revealed there was no flow going through one of the second-stage reagent valves. The problem cleared a day after a phone call.

I had to wrap up this post with an example of Greg's ever-present humor that engineers can appreciate:

It takes more and more interesting opportunities to get weathered engineers excited. However, the almost limitless opportunities to explore advanced control ideas make us downright tingly.

If you've been fighting pH control, the article is well worth it as is the "Extra-Credit Reading" they cite.

If you're an automation professional and not already subscribed to the ModelingAndControl.com blog, you're missing some great stuff.

Greg McMillan has recently posted three "sensible sensor installation" posts:

• Sensible Sensor Installation
• Sensible Sensor Installation - Follow Up
• Sensible Sensor Installation - Tips

Greg offers his rules of thumb based on his vast plant experience for installing temperature and pH sensors. Here's an example from his initial post:

The best sensitivity from a temperature or pH sensor can generally be achieved by an installation where the tip of the thermowell or electrode is in the center of the pipeline. This is particularly important when there is a high viscosity fluid such as a polymer for temperature control or concentrated sulfuric acid reagent for pH control. For temperature, it is also desirable to maximize the insertion length in the center line to reduce the thermal conduction error from the tip to the flange. The insertion of the thermowell into an elbow affords this opportunity.

I know when I was a young systems engineer I would have really appreciated more rules of thumb to give me grounding on some of the things I needed to consider. Experience teaches these things, so any shortcuts to gain these experiences are greatly appreciated.

As I mentioned in a Web 2.0 presentation at the last Emerson Exchange, many ways are emerging to share your process automation expertise. A blog is one way, but other ways include adding/modifying entries in Wikipedia, social bookmarking with Del.icio.us, and sharing interesting posts you come across with web-based RSS readers like Google Reader.

If you've not yet taken the plunge to see what subscribing to RSS feeds is all about, see the screencast of how to subscribe to this blog, and how to import my blogroll. This is my way of helping get you jumpstarted to these rules of thumb with many automation and process industry-based blogs, including Terry and Greg's ModelingAndControl.com.

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.