This post is an excerpt from the journal ISA Transactions. All ISA Transactions articles are free to ISA members, or can be purchased from Elsevier Press.

Abstract: Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional integral derivative (PID) controller. To enhance the  effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initi- alized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on- line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller.

Free Bonus! To read the full version of this ISA Transactions article, click here.

2006-2018 Elsevier Science Ltd. All rights reserved.

These days, manufacturers need to run their physical assets – especially high-capital ones – at optimal efficiency. They must do this to remain competitive in a market that demands quality despite rising manufacturing costs and stricter regulations.

Under such conditions, the maintenance managers in charge can relate with the challenge of getting funds approved for a modern maintenance software, especially since they are often under pressure to reduce existing operating costs. Usually, the difference between approval and denial will depend on their ability to prove the value such software would add to the organization’s operations and bottom line.

Reduction In Downtime

One of the most important KPIs manufactures struggle with is reducing asset downtime. Downtime takes an equipment out of use and, depending on how mission-critical such an asset it, every minute it is unavailable contributes to production delays and revenue loss.

If not properly managed, downtime alone can disrupt the entire operational output of a plant for days or even weeks at a time, bringing the entire production line to a grinding halt.

But, even in situations where some downtime is inevitable, maintenance software will reduce idle time while improving asset reliability.

This is possible because a CMMS allows the maintenance team preempt failure, plan ahead, and respond faster. It helps eliminate much of the guesswork and inefficiency of monitoring machinery manually.

How to Calculate ROI

Now, let’s see a simple example for determining the ROI pertaining to downtime by comparing two different manufacturing plants. Plant “A” uses maintenance software while plant “B” still monitors maintenance manually.

Plant B has a packing machine that contributes $150 per hour to the bottom line. But, this machine breaks down, the maintenance team is caught unaware, and the resources are not immediately available to repair it. The result is 8 days of downtime.

Cost to Fix for Plant B is: $150 x 24 x 8 = $28,800

Whereas, Plant A has the same type of machine delivering the same results, but they monitor this asset using a maintenance software that cost $5,000. They are prepared for the breakdown and downtime lasts 1 day.

Cost to Fix for Plant A is: $150 x 24 x 1 = $3,600

We can calculate ROI using the formula: ROI = Net Profit / Total Investment * 100%

In this case, (Plant B’s loss – Plant A’s loss) – Software Cost / Software Cost *100%

(28,800 – 3,600) – 5,000 / 5000 *100% = 404% ROI

Planned Maintenance Compliance

Another common challenge large businesses have is monitoring maintenance operations across different departments and multiple locations. In such cases, they can make things easier and save costs by using more of a planned rather than a reactive maintenance strategy.

To put this in better perspective, let’s look at the report by Plant Engineering.

Among several parameters measured, reactive maintenance performed woefully compared to other maintenance strategies. For instance, in preventing asset failure, planned maintenance scored 64% compared to a poor 3 percent using reactive maintenance.

Using CMMS puts an organization in a better position to run an efficient and proactive maintenance plan. In fact, it’s ability to run planned maintenance functions almost seamlessly is one of the major attractions to using modern maintenance software.

However, even the best techniques and software are really only as good as the people that will implement them. So, it’s not uncommon to find that after implementing a planned maintenance schedule, monitoring the plan and enforcing compliance becomes a new set of problems.

If the proper systems are not in place to track and measure each technician’s accountability for maintenance tasks assigned to them, it’s only a matter of time before the process is abandoned.

Fortunately, most modern maintenance software available today empowers users to easily monitor planned maintenance tasks no matter how large the company’s operations are. One way of achieving this is by generating reports to check compliance with:

• Scheduled maintenance tasks.
• On time delivery of work orders.
• Safety standards for specific jobs.

Such reports help to detect where there are inefficiencies so as to quickly correct them.

Improved Inventory Management

The most common problems manufacturers face with inventory management include:

• Prolonged downtime due to unavailable parts.
• Relying on spreadsheets to manage inventory.
• Establishing reorder levels.
• Holding excessive inventory.
• Estimating the cost of required spare parts on an ongoing basis.
• Updating and estimating the value of stock on hand in real time.
• Increased costs associated with stockout.
• Emergency order charges.

Rather than having to contend with the above problems, imagine having a centralized system that automates the process of ordering and managing inventory and spare parts.

In particular, two outstanding areas that CMMS users will get the best ROI with regards to inventory management are reduction of downtime (since spare parts are readily available) and significant reduction or elimination of the costs from overstocking.

Better Document Management

Manufacturers can expect to generate and store a wide range of documents from every unit in the company. Whenever staff have to find information, the document handling system already in place will determine how quickly they can get what is needed and return to work.

For those that still rely on physical records, the amount of paper generated can be staggering. Add to that the time lost searching for missing or misplaced documents.

A study by SearchYourCloud shows that, on average, employees may have to search for a document up to eight times before finding the information they need. Hence, organizations that still rely on archaic filing systems stand to lose a chunk of every workday.

And that’s not the full picture yet.

Some paper handling costs that companies fail to track but that can quickly add up include the assets and space required for storing physical files, cost of destroyed records (through flooding, theft, or fire), the cost of supplies, etc.

Let’s take the maintenance unit for example. The documents generated will contain information about safety reports, audits, work orders, inspection records, equipment history, vendor details, and so on. Many of these reports can date back for decades depending on how long the business has been in operation. In the worst-case scenarios, whole rooms have to be devoted to storing paper files alone.

On the other hand, a modern maintenance software will streamline this process and allow the same maintenance unit capture data, store it, duplicate it, and send it to whoever needs it all with a few clicks. Even better, technicians and anyone else looking for information can access the database from remote locations by using internet-enabled devices.

Energy Consumption Savings

Last but not least among the common KPIs for determining the ROI on maintenance software is energy consumption.

Businesses commonly find that energy consumption is their highest overhead costs after human resources. Interestingly, reducing energy consumption is possible and it is a indirect benefit of using a CMMS.

From lighting to HVAC and other high energy consuming systems, maintenance software supported with sensory equipment allows its users to asses and record the condition of their assets before they deteriorate and begin to drain energy. In essence, a CMMS that can read the incoming data from condition monitoring sensors, enables you to implement condition-based maintenance.

Going back to the example of Plant “A” and “B” mentioned earlier, we can demonstrate how maintenance software saves on energy cost for running HVAC systems.

Plant B uses time-based maintenance for its HVAC system and replaces worn parts according to the frequency dictated by the schedule. Of course, that increases the possibility of unnecessary replacements.

Plant A uses condition-based maintenance for its HVAC. The software raises alerts for repairs/replacement based on data derived from vibration, refrigerant, and oil analysis. This way, Plant A is able to save up to 20 percent of its annual HVAC costs.

Although the upfront costs of a maintenance software may seem higher than continuing with a manual maintenance plan, the ROI will be realized continually for many years ahead.

This automation industry quiz question comes from the ISA Certified Automation Professional (CAP) certification program. ISA CAP certification provides a non-biased, third-party, objective assessment and confirmation of an automation professional’s skills. The CAP exam is focused on direction, definition, design, development/application, deployment, documentation, and support of systems, software, and equipment used in control systems, manufacturing information systems, systems integration, and operational consulting. Click this link for more information about the CAP program.

Which of the following flow measurement devices does not require square root extraction?

a) magnetic flowmeter
b) venturi flowmeter
c) orifice plate
d) pitot tube
e) none of the above

The way that factories operate is undergoing a significant shift, and collaborative robotics is at the heart of this transformation. In the past couple of years, businesses have recognized this and have begun investing significantly into the new category of robotics. In fact, Goldman Sachs predicts that the collaborative robot market will be $3 to $4 billion by 2020, and will increase to $6 billion by 2025. But what exactly are collaborative robots, what are they used for, and why is their adoption increasing so rapidly?

While some may think that collaborative robots are used as personal assistants (think Rosie from The Jetsons), most collaborative robots on the market today are used in manufacturing settings to work alongside humans on the factory floor. According to Boston Consulting Group (BCG), 90 percent of manufacturing tasks still cannot be easily automated. However, collaborative robots are working to fill in these gaps and automate processes on the factory floor, giving manufacturers flexibility to easily change their product lines and meet the demands of the market.

These robots are adept at tasks that require adaptability and work well in areas such as packaging and machine tending. Collaborative robots are built to emulate tasks that have traditionally been difficult to automate, and their features and programming are designed to perform tasks as humans do. The “collaborative” in their name refers to their unique ability to work alongside and in tandem with humans. Unlike traditional robots, if a collaborative robot comes into contact with a human, it will immediately stop what it is doing. This advanced sensing technology and safety mechanism ensures that humans can safely work alongside the robot on a production line.

Collaborative versus traditional robots

Traditional industrial robots have been critical to the evolution of manufacturing and have helped automate tasks with speed and efficiency. Programmed to do a single task at high volume, they can handle large payloads and efficiently pump out parts quickly. They have been essential for scaling factory operations.

However, traditional robots have also left a significant gap. They take hundreds of hours to program and require an automation engineer or computer scientist to implement them. These machines cannot easily be reprogrammed to a new task, and the costs in both time and money are significant if market demand shifts to a new product. Additionally, many of these traditional machines are dangerous in close proximity to humans, because they handle large weights and are oblivious to the environment around them. To mitigate these risks, traditional robots are typically required to function inside safety cages.

Advanced collaborative robots can sense if they require adjustment, and they reorient themselves accordingly. As a result, the robots can safely deal with variability on the factory floor and perform tasks like humans do. Traditional robots are presented with parts that are uniform and tasks that never change. Therefore, if the robot or a part is bumped out of position, the robot will continue to drive the task until completion, potentially damaging parts, machines, or other robots. This situation can cause a major disruption to the production line and operational schedules. Conversely, if a collaborative robot is bumped out of position, it has the capability of sensing that it is misaligned and will correct itself or pause its task until fixed.

Embracing flexible automation

For manufacturers, one of the most significant advantages of using collaborative robots is the ability to shift quickly and easily between tasks, allowing them to adjust to market demands at a moment’s notice.

Collaborative robots learn a task through training by demonstration. Flexible actuators and advanced sensors built into the robots allow them to be trained by mimicking a human’s movements. By grasping the “wrist” of the robot and showing it how to perform a task, a human can have the robots up and running in minutes, far from the number of weeks it takes to fully implement a traditional robot. And since these robots can be trained by anyone, a product specialist is not required to be on site to alter the programming. This flexibility enables manufacturers to use the robot on multiple lines and easily reprogram it if market demand changes.

The flexibility of collaborative robots is particularly useful in manufacturing environments with a lot of variability, which is becoming increasingly normal in today’s era of consumer-driven personalization and customization. Built to thrive in low-mix, high-volume environments, traditional robots were designed to accomplish one task repeatedly, and thus are stationary and inflexible. They need a workspace built completely around them in an environment that is controlled and unchanging. If there is any variation in their workplace, traditional robots stop working effectively, causing delays not only to the robot’s assigned tasks, but also to the tasks of the specialist who now has to stop and adjust the robot.

Advanced collaborative robots can “feel” their way into fixtures. Their robot positioning system allows them to realign themselves and continue their given task if anything in the environment changes, such as a minor bump of the work station. These robots efficiently switch between tasks such as packaging, machine tending, and circuit-board testing, all of which require vastly different movements.

All about the software

So what allows the new generation of collaborative robots to be so different? Fundamentally, the approach to these collaborative robots is that they are built to be a sophisticated software solution inside a robot hardware body.

The robotics industry has a long history of focusing on hardware. That paradigm is shifting as collaborative robots focus on software that will drive the next generation of the industry. It is software that allows them to:

• be trained by demonstration and operate in conjunction with humans
• master the human ability to apply the right amount of pressure to respond and react to their environment
• work in a flexible, nonstagnant environment
• apply logic and make inferences

The software-first approach to manufacturing technology is shifting the way companies operate. All of these advances are already changing the way manufacturers deploy automation—bringing robots that improve efficiency and productivity to tasks that have been out of the reach of traditional industrial robots.

IIoT and the factory of the future

As collaborative robots and machines are built with more intelligence, the factory of the future will become a paragon of the convergence of data, machines, and software. Manufacturers will want to leverage the benefits from big data and the Industrial Internet of Things (IIoT) to bring together disparate machines for smoother operations and increased efficiency.

BCG estimates that there will be a 150 percent increase in automated tasks in the next 10 years. As many countries work on initiatives that are driving manufacturing growth, such as Germany’s Industrie 4.0 and Made in China 2025, collaborative robots will be crucial to automate production lines for manufacturers worldwide. Collaborative robots will continue to integrate with more complex artificial intelligence and machine learning, and will work with new technologies like 3D printing to create a factory of the future that will give manufacturers agility in their markets while driving growth on a global scale.

A version of this article also was published at InTech magazine.

Many organizations are being asked to “do more with less.” As a result, key individuals are often overburdened, and companies are not prepared to capture knowledge from experienced employees before they move or leave—major challenges that can be disastrous if overlooked.

If you had an employee nearing retirement age who decided to leave, do you know how to secure all the knowledge that goes with him or her? Chances are, you would be “just in time” or “just too late.” Now, think further—do you have intuitive and efficient workflows that still allow you to get the work completed in a timely and safe manner?

Whether you answered “just in time,” and you have in place a knowledge transfer method—or you answered “just too late” and would watch all your working processes walk out the door—you will benefit from the information below.

One strategy is a Japanese quality approach called kaizen. By definition, kaizen is a long-term continuous improvement approach to work that systematically seeks to achieve small, incremental changes in processes to improve efficiency and quality.

How does this apply to calibration? Well, in calibration, the systems you use matter. Having smart systems that can capture years of experience and secure it for others is crucial. However, knowledge and workflows can matter more. When it comes to your overall calibration process, the strategies you deploy and the way you plan them are just as important. Hopefully, you are not walking a maintenance tightrope, cutting out calibration to seemingly lower costs. I hope you do not come to know the very expensive and catastrophic impact of reactive calibrations. This is another topic altogether!

Going back to the kaizen approach, if you want a long-term, stable improvement, you must consider your workflows. At best, a manual calibration with a single-function calibrator and keyed in results is Two Sigma in quality. But your goal should be Six Sigma. You can achieve this with automated calibration, which minimizes risks, saves time, and maximizes quality and efficiency. But what is that really? It is a fully automated process that uses multifunction, documenting calibrators integrated with software for planning, performing, managing, and documenting calibrations.

How can you get there? Use systematic steps, the kaizen approach. For instance:

• If you are using single function, consider multifunction. There are fewer tools to carry in the field, so you can get around quicker and easier. Costs are lower when there is less equipment to maintain and send in for recalibration every year.
• If you are using manual calibrators, consider documenting calibrators. They save time and decrease the risk of human error, because you do not have to record and key in data.
• If you are using pen and paper to record results, consider calibration software. Not only is risk reduced, but data can be further analyzed to help you make the best decisions about important parameters like tolerances and intervals.
• If you are using a calibration software, consider integrating it with your maintenance management software. Save the time used to manually close out work orders, give management the information it needs, and keep important instrument history details.

It might seem as though extra emphasis has been placed on systems. Yes, but the systems you use have an extraordinary impact on your process. A fully integrated calibration system (i.e., documenting calibrator and software) cuts calibration time in half. This is especially important during a maintenance outage, where time is your biggest commodity. As a whole, the right systems paired with a fully integrated software system make scheduling and strategy deployment seamless. Although it is good to take a systematic look at each small, incremental step in the process, it is not always necessary to take small steps toward change. You can go from a manual system to an automated system in one big step, with professional guidance.

The bottom line is whether you are just in time or just too late, it is possible to improve. By using a kaizen approach and coupling deployment of resources with knowledge capture, your workflows will become more intuitive and efficient. You will be better prepared for the challenges of tomorrow.

A version of this article also was published at InTech magazine.

Image Source: Wikipedia

This automation industry quiz question comes from the ISA Certified Control Systems Technician (CCST) program. Certified Control System Technicians calibrate, document, troubleshoot, and repair/replace instrumentation for systems that measure and control level, temperature, pressure, flow, and other process variables. Click this link for more information about the CCST program.

A valve positioner is usually recommended as an accessory to a control valve for which situation described below?

a) for control valves with bodies over 2 inches (5 cm) in order to overcome actuator spring forces
b) for control valves that are constructed of carbon steel or bronze
c) for control valves that are experiencing a large degree of hysteresis
d) for split-body control valves
e) none of the above

This post is an excerpt from the journal ISA Transactions. All ISA Transactions articles are free to ISA members, or can be purchased from Elsevier Press.

Abstract: Chattering alarms are the most found nuisance alarms that will probably reduce the usability and result in a confidence crisis of alarm systems for industrial plants. This paper addresses the chattering alarm reduction using median filters. Two rules are formulated to design the window size of median filters. If the alarm probability is estimated using process data, one rule is based on the probability of alarms to satisfy some requirements on the false alarm rate, or missed alarm rate. If there are only historical alarm data available, the other rule is based on percentage reduction of chattering alarms using alarm duration distribution. Experimental results for industrial cases testify that the proposed method is effective.

Free Bonus! To read the full version of this ISA Transactions article, click here.

2006-2018 Elsevier Science Ltd. All rights reserved.

This article overviews some of the benefits and methods of state-based control as it applies to the models defined by the ISA106, Procedure Automation for Continuous Process Operations standards development committee. It describes how procedure-based automation can enhance your adherence to alarm management best practices and coordination of safety instrumented functions

The problem

As continuous process industries have evolved, an increasing number of functions have been relegated to the basic process control systems (BPCSs). Yet many engineers in the industry only leverage the full capabilities of the BPCS once the process has reached steady state (e.g., normal operation). The migration of the process through its transient states has frequently been managed manually.

Manual operation of the procedures during these transients often results in a plant that is more costly to run and maintain. If these tasks are not properly documented in standard operating procedures (SOPs), this methodology requires key personnel to carry out the procedures. The lack of documented procedures is exasperated by reductions of the workforce and the increasing median operator age, so much of this knowledge is being lost from the plant floor.

Consider a startup operation for a process. Even with well-documented procedures, operators may be required to manually apply bypasses and ignore extraneous alarms and some key performance indicators (KPIs), while paying close attention to others as they determine when it is appropriate to advance to the next step.

As a result, plant operation is often adversely affected. Costs increase due to:

• increased exposure to risks and hazards (reduced safety) in the process
• increased operator workload and reduced operator awareness
• increased losses and reduced product quality
• longer startups and shutdowns
• more trips

The solution

Applying the procedure-based automation methodologies set forth by ISA106 to the continuous process solves many of these problems by capturing knowledge frequently held by key personnel and identifying tasks to be executed, along with their initiating commands and rules for verification.

Many plants have written standard operating procedures. However, each operator must develop his or her understanding of the procedures when introduced to the system. This leads to inconsistent (or worse, incorrect) execution. Although the ISA106 technical report supports manual implementations, companies can achieve greater benefits by automating procedures.

If a procedure can be documented, it can be automated. Automated procedures help capture more detailed process knowledge, so operators can react more quickly to changing conditions in the process. This in turn leads to a more repeatable process, higher-grade products, quicker turn arounds, and reduced operator workloads..

To achieve an accurate representation of the application, the engineer must address all phases of the process. He or she needs to manage the coordination of the state the system is in and the tasks that are to be executed. This management is frequently referred to as “state-based control.” Applications within the continuous process industry have always had states that the plant, system, or process transitioned through on its way to the fully operational “production” state and back down to the “stopped” state.

A state is any well-defined phase in which a system can exist. The phase can be either categorized as “steady state” (e.g., distillation) or “transitional” (e.g., heating up), where a specific value (or values) is being controlled to a desired set point. Examples of states include stopped, ready to start, starting, production, stopping, and fault. Consider the following simplified state-based control strategy (figure 1).

Figure 1. Simplified state-based control strategy

Unlike a typical sequence of a batch system that is executed and ultimately completed, a state-based control system is always in one (and only one) of its defined states at a time. At initialization (system reset), the system’s program is forced into the “stopped” state to ensure proper output commands (e.g., valves closed and pumps off). When the permissions are satisfied, the system goes to the “Ready_To_Start” state. When the StartCMD is received, the system leaves “Ready_To_Start” and goes to the “starting” state.

The system moves along the path through its states as the conditions of each transition are satisfied. These conditions include current state as well as KPIs and may additionally include operator acknowledgement or interaction. Program execution continues through the sequence until it returns back to “stopped” for another system cycle. The “transitional” states may be short lived with the plant spending most of its time in the production state, but it is essential to capture all the intermediate transitional states to complete the model.

The above example is a simplified circular loop and does not address any exception handling, such as trip conditions, abort commands, or emergency stops. State-based control typically has multiple branches where states can be entered from more than one adjacent state. Consider the following extended example (figure 2).

In this example, the normal execution is carried out along the outer loop. If a fault condition exists that warrants going to a special fault state, execution jumps to the “fault” state. The system “holds” here until the recovery command (RecoverCMD) is received. In the fault recovery step, the program executes steps that will prepare the system to be started again. This may include draining poor quality product to a waste tank, flushing a pipe, or coordinating with another system.

The result is that based on the state that the system is in, commands to initiate tasks and enable different trip conditions and drive final elements can be readily controlled. Additionally, operator displays can be decluttered and alarm flooding reduced through implementing ISA-18.2 Alarm Shelving based on the current state.

What is the ISA-106 technical report?

ISA-TR106.00.01-2013 – Procedure Automation for Continuous Process Operations – Models and Terminology was written to facilitate consistency of procedure-based automation within the continuous process industries. It specifies a set of terms, definitions, and models to be used as best practices in the design phase of the system’s life cycle. The technical report defines three models for categorizing and organizing the objects of the system: physical, procedure requirements, and procedural implementation (figure 3).

Physical model: The physical model maps the role-based equipment model functional levels 0 and 1 of ISA-95 into the continuous process. Each component in the model may contain zero or more subordinate components. The plant area might be the polymer section of the plant. Examples of units are a distillation column and a reactor; examples of devices are valve positioners and pumps.

Procedure requirements model: The procedure requirements model maps into ISA-95 functional level 2 and defines how the procedure requirements map into the hierarchy of the physical model. In the components of this model, it details what is required to accomplish an objective. An example of a unit procedure requirement is for a reactor to bring the temperature of its contents up to a prescribed temperature or perform a controlled shutdown during a process upset condition. An example of a control procedure requirement is turning on a pump or closing a valve.

Procedure implementation model: The procedure implementation model maps into ISA-95 functional level 2 and represents the configured or programmed result of the implemented procedure requirements. The linkage, mapping, and traceability of the implementation module to the requirements is not necessarily one to one. A requirement’s objective can be achieved by multiple implementation modules. Similarly, a single implementation module may satisfy multiple requirements.

Implementation modules contain at least one task and may contain composite tasks (tasks within a task). Each implementation module defines the command that initiates its execution, the steps required to be performed, and the detailed criteria to verify that the requirement result has been achieved or failed.

Example of implementation module block diagram

Commands, tasks, and verification can be implemented in a number of automation styles: manual, computer assisted, or fully automated. Consider a reactor agitation task that is to be started once the reactor contents are at temperature:

• Manual: The operator executes the command (e.g., pushing an AGITATE button to begin).
• Computer assisted: The operator receives a prompt from BPCS that the temperature is at the desired set point, and the operator acknowledges to allow the advance to the AGITATE step.
• Fully automated: BPCS automatically sequences to the AGITATE step once the temperature set point has been reached.

Use cases

Example 1: Conventional BPCS

In a regulatory control BPCS, the implementation modules of the procedures can be managed by a state-based control engine developed in any of the languages available to the programmer. Typically, this is accomplished using a sequential function chart (SFC) that executes in a looping function, preventing the termination of the sequence. Each step of the SFC represents a state used to call its associated tasks. The tasks can be called directly, or the state can be forwarded to additional logic to control the final elements and monitor PVs. Tasks can be implemented as subsequent SFCs or control strategies.

Based on the indicated state, alarm functions can be coordinated to satisfy ISA-18.2 alarm shelving requirements.

Example 2: SIS with BPCS 

In one recent implementation, the company needed to address the coordination of the chemical reaction between the BPCS and a safety instrumented system (SIS). The reaction is brought to the production state through a hazardous transient phase/state. The reaction is highly exothermic and requires a high-temperature trip condition for the “production” state. However, the “startup” state required a higher high-temperature trip and rate of change condition. The alarm management strategy also required the suppression of nuisance alarms on a per phase basis (e.g., low tank level alarms while filling during the startup state). Similar requirements existed for the “stopping” phase/state.

From the above requirements, certain trip conditions (safety instrumented functions [SIFs]) and alarms needed to be bypassed in some states and enabled in others. Because these SIFs were implemented in an SIS, the company needed to disable and enable the coordination of the state-based control in the SIS to protect the validity of the SIL calculations and compliance to ISA-84. The SIS provided this coordination and selectively disabled and enabled the associated SIFs based on phase.

The phase and state information was additionally communicated to the BPCS for its regulatory control of task requirements. With this technology, the engineer could automate the startup and shutdown of the system with confidence in a uniform response. Without it, the task to enable or disable bypasses of trips and alarms would rely on strict operator adherence to documented SOPs during high-stress conditions.

Benefits

The benefit of automating with state-based control in a continuous process is the reduced cost of plant operations. These cost reductions come in the form of risk reduction (improved safety), better product control, improved alarm management, reduced operator work load/fatigue, and improved visibility into plant asset management. The key benefits are:

• better safety (risk reduction)
• improved alarm management
• reduced operator work load
• maintenance planning with asset management

Improved safety is one of the key benefits of state-based control. The startup and shutdown operations of the continuous process are arguably the states when a dangerous demand to trip is most likely, yet these states are frequently manually monitored and controlled. Couple this with the high demands on the operators to achieve full operation, and mistakes are more likely to occur. If the trip conditions and their responses for the transient states are different from the operational state and can be well defined, they can be automated in the logic. Automating these reduces the operator work load and minimizes the chance of missed steps (e.g., removing a bypass when startup is complete) documented in the SOPs.

Alarm management is a critical issue in the industry, receiving much scrutiny at the corporate level. With state-based control, the alarms that are not appropriate during specific phases of the plant can be “hidden,” using alarm shelving techniques as defined in ANSI/ISA-18.2-2016, Management of Alarm Systems for the Process Industries. While alarms are hidden from the operator, they are still recorded in the journal system for completeness. The net result is operators are not distracted with nuisance alarms and can retain focus on the tasks they are charged with performing.

The automated nature of state-based control typically reduces the time-to-normal operation through infrequently executed states and grade changes. This type of control also generates higher quality products and achieves more repeatable production.

With state-based control, maintenance of the plants assets can be better predicted. Starting and stopping devices is typically harder on them than continuous operation. Having visibility into these demands allows scheduled maintenance, reducing unplanned plant downtime due to failures.

Control for all phases

The state-based control methodology of ISA-TR106.00.01 is an effective way to control a system during all phases of the system’s operation. It does require a shift in requirement documentation from the SOPs to the automation system. In the traditional methodology, the cost of defining these subordinate states, tasks, and requirements is often lost or buried in the development of the SOPs. There is no escaping it; they must be defined somewhere.

Bear in mind if the requirements can be documented, they can be automated in the controller’s program. The benefits of automating are a more repeatable system performance, safer systems, improved alarm management, and reduced operator workload.

A version of this article also was published at InTech magazine. 

This automation industry quiz question comes from the ISA Certified Automation Professional (CAP) certification program. ISA CAP certification provides a non-biased, third-party, objective assessment and confirmation of an automation professional’s skills. The CAP exam is focused on direction, definition, design, development/application, deployment, documentation, and support of systems, software, and equipment used in control systems, manufacturing information systems, systems integration, and operational consulting. Click this link for more information about the CAP program.

According to ISA-88.00.01-2010 (Part 1), which statement is true about the models of control that can be used to describe a batch process:

a) ISA-88 describes four models: process, procedural, equipment, and physical.
b) A process stage in the process model can be mapped to a unit procedure in the procedural model, which in turn is mapped to a unit in the physical model.
c) Procedural control model elements are designed to correspond to elements of the physical model.
d) A unit (physical model) may support procedures, unit procedures, operations, and phases from the procedural model.
e) None of the above

This is Part 1 of a series on the benefits of knowing your process and PID capability. Part 1 focuses on process behavior, the many loop objectives and different worlds of industrial applications, and the loop component’s contribution to the dynamic response.