Leveraging IIoT for process optimization, modernization
Industrial facilities need combined production and maintenance strategies using the Industrial Internet of Things (IIoT) and digital twin tools to minimize unscheduled shutdowns and optimize product quality while cost-effectively using existing operations, maintenance and engineering resources.
- Examine how digitalization and Industrial Internet of Things (IIoT) can deliver 10% to 25% gain in key metrics.
- Understand that asset failure and imperceptible reductions in process and equipment efficiencies are constant threats to the operating plan and overall equipment effectiveness (OEE).
- Apply sensors, automation systems and cloud technologies a smarter, IIoT-enabled plant.
Need 10 to 25% gains in key industrial metrics? In a competitive global marketplace, industrial organizations seek “digital intelligence” to manage and operate hundreds or even thousands of assets, from one site or across an enterprise, to address critical operating demands. They also need effective tools to transform process data into real-time information regarding process performance, equipment health, energy consumption and emissions monitoring. Digital intelligence tools include process and event data collection, combined process and asset-centric analytics, and visualization technology to continuously and automatically collect, organize and analyze data. Smarter plants use sensors, automation systems and cloud technologies integrated with current systems and data analytics.
Plant operators, process and equipment engineers and managers require continuous monitoring and surveillance, notifications and collaboration with experts so appropriate proactive actions can be taken. This activity will minimize degradation, poor performance and secondary damage to equipment to reduce costs, as well as increase throughput and profits.
10 to 25% gain in key metrics: IIoT industrial benefits
Rapid adoption of the IIoT has created economies of scale for smart sensors, connectivity, analytics and robust software platforms. This change is driving the adoption of enterprise-level performance management, process monitoring, predictive maintenance programs and business transformation, with the goal of eliminating unplanned downtime and reducing operating costs while maintaining product quality and compliance.
A real-world approach to the IIoT enables the integration of current systems and the addition of new data sources and analytics to support complementary, continuous improvement processes focused on performance monitoring and decision support. The specific benefits of this approach include:
- Increase process reliability and asset utilization up to 10%: Plants can reduce unplanned downtime by defining and operating within operating and integrity envelopes, predicting failures and providing proactive responses, as well as minimizing rate and efficiency losses.
- Increase operating efficiency up to 10%: Industrial organizations can manage performance, including yields, energy and raw materials usage, to achieve up to a 10% reduction in costs. This results from enhanced engineering and production effectiveness with continuous monitoring, remote collaboration and ready access to required information, as well as improved decision support.
- Sustain advanced control and preventable degradation with benefits up to 25%: Control teams can maintain the effectiveness of control loops, controllers and models; adjust controls to new operating conditions and process changes; and address critical instrument issues.
- Increase safety: Production facilities can minimize risks by ensuring normal and stable operations, and eliminate production stops for safety system verification.
- Reduce maintenance costs up to 10%: Operations teams can take proactive measures to minimize equipment damage and emergencies while optimizing maintenance based on real asset conditions, thus improving reliability and extending equipment life.
Moving beyond traditional industrial benefits
To ensure uptime, companies have historically sent field technicians out to perform routine diagnostic inspections and preventive maintenance according to fixed schedules. Unfortunately, this approach is a costly, labor-intensive process with little assurance failure will not occur between inspections.
To improve efficiency, companies have implemented advanced process control (APC), defined operating boundaries with their alert system, created key performance indicators (KPls) and called upon local experts to help solve operating problems. The effectiveness of these measures has been difficult to sustain as they rely on dedicated and knowledgeable onsite personnel.
In addition, industrial firms are looking for ways to make sense of vast quantities of data that can have a significant impact on their performance. For example, reporting and interpreting alarms and alerts is central to safe operations. It is also important to act upon abnormal situations effectively. To support the variety of monitoring and decision support applications necessary within a manufacturing facility, data needs to be turned into information and delivered with context so it can be understood and used in a myriad of ways by various people.
5 key operational objectives for operation, OEE
For manufacturing and other operating companies, asset failure and imperceptible reductions in process and equipment efficiency are constant threats to the operating plan and overall equipment effectiveness (OEE). As a result, they are shifting their spending to increased equipment maintenance, thus losing potential revenue. Factors such as the availability of skilled workers and increasingly complex production processes are impacting the ability to predict and detect deteriorating asset health and process performance.
To maximize their overall performance, modern plants are looking for ways to transform operating and maintenance philosophy from reactive, to proactive, by aiming to keep operations running as efficiently and steadily as possible while decreasing unplanned downtime. Key operational objectives include:
- Deploy online, continuous monitoring and exception-based alerts for process performance, equipment and controls.
- Capitalize on increased data availability across the enterprise.
- Put data into context to compare assets to determine similar conditions or behavior.
- Implement tools for process and reliability engineers to enable visual data exploration to decrease reliance on complex machine learning algorithms to solve problems.
- Establish collaboration with internal and external subject matters experts (SMEs).
Integrated operational and maintenance strategies open new possibilities for companies. Data from sensors monitoring process and machine conditions are combined to identify any patterns that indicate a possible fault or process limitation. This monitoring allows the onset of a stoppage to be recognized early, and corrective measures to be planned and introduced in the most effective way.
Combining process and equipment data leads to understanding asset capability, and allows for accuracy, equipment integrity and many other useful insights that can be used throughout APC strategies. The result is greater process stability within control and monitoring systems for situational awareness at all levels of operations, as well as improved decision support systems to ensure assets are operated in an optimal manner. With this approach, unplanned downtime can be avoided and staff and resources can be employed more effectively.
Leveraging the industrial internet, aging equipment, retiring workforce
The IIoT carries major implications for the manufacturing industry, especially at a time when infrastructure is aging, and veteran operators and engineers are retiring. There is a shortage of experienced workers to take the place of seasoned personnel, resulting in a loss of knowledge. The IIoT can be leveraged to institutionalize knowledge capture while requiring fewer internal experts. This operation can be done with the help of external experts, such as process licensors, who have expertise and visibility beyond the company’s assets. The IIoT also can have a significant impact on competitiveness for manufacturers.
The IIoT allows companies to do more with current systems and extend business processes to enhance monitoring and reduce the time to action. For example, a cloud-based control loop and APC monitoring system can be set up to monitor controls across the enterprise by an internal or external domain expert. Visibility and knowledge across sites helps experts alert and collaborate with onsite experts and recommend actions when control benefit degradations are detected. Each site can benefit from earlier detection and faster resolution of problems afforded by a higher level of expertise focused on control performance. For the enterprise, these capabilities can be deployed using fewer resources than having an expert at each site.
5 IIoT benefits for manufacturers
To make better business decisions, the IIoT offers companies the ability to:
- Aggregate data from existing sources
- Create additional data sources in a cost-effective way
- Gain visibility into new data
- Identify patterns
- Derive insight through analytics.
Through this approach, previously unsolved problems, as well as new ones, can be solved with assets communicating and providing real-time usage data to allow plants to do predictive maintenance and process optimization.
Industry-leading companies are transforming their operations by using proven solutions in the areas of process and event data collection, combined process and asset-centric analytics, and finally, visualization technology to continuously and automatically collect, organize and analyze data. Advanced analytics is one of the pillars of the IIoT connecting people, processes and assets to optimize business results. It can transform work processes from manual and reactive to automatic and proactive, helping users avoid unplanned downtime and improve performance and safety.
Sensors, automation systems, cloud technologies
An IIoT-enabled plant uses a combination of advanced sensors, automation systems and cloud technologies integrated with current systems and data analytics to become smarter. This setup provides the ability to locate data in a cloud environment where it can be accessed and analyzed with analytical tools. For example, an equipment vibration reading would be sent to the plant’s distributed control system (DCS) as one value, whereas rich dynamic data stored in the cloud would allow engineers to study the harmonic signature of a bearing or shaft to determine the root cause of a pending asset failure. In most cases, dynamic data is employed by specialists in custom applications—limiting its accessibility by other users in the plant.
In terms of predictive maintenance and process performance, lloT-based solutions enable industrial enterprises to manage their assets and make more informed decisions through analytics at the edge. Production and maintenance strategies can be combined for an overall optimal performance and executed based on how assets are expected to function tomorrow—not solely according to a specific period or present conditions.
Another key driver of the IIoT is a reduction in the level of information technology (IT) skills and expertise required to support standalone applications so companies can focus on their core competency of running and managing operations.
Making the most of plant data using real-time analytics
Major automation suppliers have developed innovative technologies that deliver real-time process and asset-centric analytics, performance calculations, event detection and collaboration for plant management, engineering, maintenance, center of excellence (COE) experts and operations. These solutions are designed for online continuous monitoring of equipment and process health, enabling industrial facilities to predict and prevent asset failures and poor operational performance.
Tools for real-time process performance monitoring provide statistical calculations and embedded performance models, which, when paired with near real-time surveillance of instruments, processes and equipment, allow users to assess asset performance. They offer a clear window into plant processes. Continuously monitoring operating conditions and enables decisions and actions to prevent production loss, minimize downtime and reduce maintenance expenses.
The latest developments in the field of plant equipment and process health monitoring leverage secure, managed and hardened edge-to-cloud platforms while focusing on data science and analytics and applying “digital twin” patterns to drive their analytical models. With the help of external experts, these solutions enable industrial firms to extract meaningful insights from their data. This leads to improved decision-making and addresses such issues as safety improvement, asset management and optimization of operations. As a result, process plants are becoming more agile, driving increased revenue and keeping the focus on what matters most to production.
Unlike condition-monitoring solutions focused on a piece of equipment’s physical condition, the latest data analytics and asset monitoring solutions use performance degradation as a leading indicator of potential problems. For example, with the IIoT, identification of performance degradation and actions to be taken is improved since processes and equipment data are used not only for a specific compressor but also for all compressors of similar design and service. Some tools employ pre-defined best practice templates for a wide range of equipment types, including pumps, compressors, exchangers, valves and turbines. Combined with an interface to process design simulation software, this solution helps users deploy equipment or process monitoring on any plant asset, eliminating the need for complex model development.
IIoT, digital twins: 5 measurable gains
It is important to remember the lloT is not just about capturing sensor data. Information needs to be put into the asset context structure; merely operating on tag-based data will not ensure a repeatable and scalable solution. Processes are instrumented for control rather than reliability or optimization, and, as a result, much of the “derived data” that’s important for prediction and decision-making is locked in spreadsheets and other standalone tools. It is essential to continuously calculate this data and bring it into the IloT environment where continuous runtime analytics can examine historical performance for use in machine-learning algorithms.
Furthermore, IIoT solutions should not rely on a statistical model to detect deviations from normal. Having a fundamental, physics-based model creates a digital twin, with a virtual representation of the process or asset located in the cloud. This setup allows users to model and compare expected process performance against actual results, and then apply these deviations as early indicators of health degradation.
Digital twins exist at the intersection of physical engineering and data science, and their value translates to measurable business outcomes, such as:
- Reduced asset downtime
- Lower maintenance costs
- Improved plant and factory efficiency
- Reduced cycle times
- Increased productivity.
Takeaways: Asset management, operational analytics
Organizations across the process industries are seeking to improve their return on large asset investments. However, effectively managing assets requires a wealth of information and analysis. Industrial facilities need combined production and maintenance strategies to minimize unscheduled shutdowns and optimize product quality while cost-effectively using the operations, maintenance and engineering resources they have on hand.
The true value of the lloT can only be fully realized with a holistic view of asset management. Powerful virtual cloud networks will continually collect, aggregate and model data for accurate prediction of degradation and failures and put contingencies in place to limit their impact on system availability. This approach is becoming fundamental to improving process reliability and driving cost takeout by delivering real-time, intelligent and actionable data to connected systems and the end user. Although it may take time for some companies to become an IIoT data-driven organization, the evolution is here, and they should begin preparing for it.
KEYWORDS: Industrial digitalization, asset management, analytics
Is your plant optimizing digital intelligence to deliver operational gains?