Wireless Automation for Sustainability
Sustainability managers, energy managers, production managers, and their teams of engineers, supervisors, and technicians work to keep the plant green. We need to give them great tools to be successful. Industrial Internet of Things (IIoT), digital transformation (DX), and Industry 4.0 are heralded as key solutions and methodologies, and they are, and all of them are about automation. The 22nd of April is the Earth Day so like in past years I’d like to share some ideas on how to make sustainability and energy management in plants easier by providing personnel with new automation tools. Let’s examine how the work of sustainability and energy engineers is changing with wireless sensors. And it doesn’t have to be difficult. It can be done by sustainability and energy engineers working with your I&C team. What are the recommended practices for the I&C team to build and support these solutions to provide real-time quantification and indication of equipment performance? Here are my personal thoughts:
The most important fact that many have missed in the past is that the wireless sensor network (WSN) technology you choose makes a huge difference in the long-term viability of these performance and emissions monitoring solutions. With the wrong WSN you cannot pull current information from sensors on demand when you need it, and you cannot easily integrate the sensor measurement data into analytics and other third-party platforms and apps without costly coding and scripting.
Plant Challenges
Sustainability and energy challenges in a plant include overconsumption which leads to energy and utility cost, carbon footprint, and carbon tax. Venting and flaring greenhouse gas (GHG) emissions which leads to energy cost, product cost, and fines. Leaks and other losses which also leads to energy and utility cost. Preventive cleaning and overhaul have associated scheduled downtime (some of it unnecessary) and preventive cleaning and overhaul cost (some of it unnecessary). Inspection rounds is another function with associated inspection and performance testing cost (transport, outsourced services) and productivity numbers. Many of these stems from manual practices like manual data collection walking the plant and manual data interpretation. This includes reading flow meter registers, and checks using portable testers for acoustic nose for release, venting, and internal passing. Other manual checks with portable testers include temperature gun. This is time consuming and labor intensive so manually checking hundreds or thousands of positions even monthly or even yearly in large plants is a real challenge. Monthly data collection is too infrequent to be responsive. This causes delays. But daily manual checks would be impractical. These are the reasons why plants want to change how sustainability and energy is managed.
Work Transformation
The new ways of working, of managing sustainability and energy include automatic data collection, automatic data interpretation, and automatic workflow. That is, energy management, emissions management, performance management, and loss control. This is useful in any plant, but particularly helpful for normally not manned sites like offshore platforms and remote wellheads looking for autonomous and centralized operations. This is the vision of how plants want to change how sustainability and energy is managed. Now, just like you can’t drive your car looking at the refueling log, you must look at the real-time indicators on the dashboard, you cannot run a plant looking at the energy bill either, you need to provide real-time indicators.
The New Automation Solutions
To achieve this vision, plants deploy Industrial Internet of Things (IIoT) and digital transformation solutions which is just another name for automation solutions. Most of these solutions are a combination of ready-made analytics software for the automatic data interpretation and underlying sensors for the automatic data collection. This is a second layer of automation, beyond the P&ID, what some refer to as hyper-automation. Engineering is where the vision statements of holistic transformation come down to implementation to address individual use-cases. Popular examples of performance monitoring solutions include packages for steam traps, pressure relief valves (PRV), air-cooled heat exchangers (ACHX), cooling towers (CT), heat exchangers (HX), methane leaks, industrial lighting, relief vents, thief hatches, and automating meter readings for energy management. The recommendation is to start with these use-cases, but there are more.
Heat Exchanger Monitoring Solutions
Heat exchanger inefficiency include fouling of heat transfer surfaces on hot and cold side as well as plugging if allowed to go on for prolonged periods of time. These inefficiencies, first principles (1P) thermodynamics, and metrics which are measures of inefficiency are well understood.
Software
Software, an analytics app, based on Artificial Intelligence (AI) is used to automatically quantify fouling factor, duty error, heat transfer coefficient, and its rate of change in the heat exchanger. This is information useful to optimize the time of cleaning: not to late causing inefficiency, not to early causing unnecessary production downtime. This reduces the need for cooling water or downstream makeup heat. The recommendation is to use a specialized heat exchanger app with special features for heat exchanger analytics.
This is analysis and quantification that in the past required a human with expert know-how. By automating the analysis, it gets done continuously, not just once a year. This was not possible with a manual process. With these automatic tools, personnel can manage many more pieces of equipment than what was analyzed manually in the past. Experts can focus on the pieces of equipment requiring attention, without wasting time on those that don’t.
The recommendation is to use readymade apps using mechanistic AI embedding the well-known first principles (1P) thermodynamics to quantify using real-time sensor data. Even estimate losses incurred. With this approach years of historical data, data cleansing, algorithm training, modelling, and testing is not required. The sustainability and energy engineers are responsible for the efficiency of the equipment and therefore need this information in software on their laptop at their desk, so this information shall be displayed in apps for them, not in the DCS operator console in the control room, because that is not where they work. It’s the same app framework for the various kinds of equipment in the plant.
Sensors
The recommendation is to add temperature, flow, and differential pressure sensors on both hot and cold sides to take the place of manual rounds to automatically collect the data required by the analytics for accurate and timely quantification. An important point is to use a quad input temperature transmitter for time synchronized temperature differentials (ΔT) between inlets and outlets on both the hot and cold sides. The exact configuration of sensors depends on the performance metrics you need. For heat exchangers with multiple bundles, this is done on a per-bundle basis to enable fouling to be pinpointed to a specific bundle which can be bypassed and cleaned while the other bundles remain in service. Sustainability and energy engineers work together with the I&C engineers to determine which sensors are required on each piece of equipment.
This is data collection and input that in the past required a human to go out with portable testers or reading mechanical gauges. By automating the data collection, it gets done once a day, once an hour, once a minute or every few seconds - not just once a year. This was not possible with a manual process. Sensors are permanently installed on equipment in every corner of the plant. With these sensors, personnel can “inspect” many more pieces of equipment than what was covered manually in the past.
The recommendation is to add wireless and non-intrusive sensors as they require no power cord, no signal wires, and no I/O cards. And there is no cutting, drilling, or welding required for installation of many of these sensors. That is, many of these sensors can be installed while production is running.
Cooling Tower Monitoring Solutions
Cooling tower inefficiency is principally fill-fouling. The inefficiencies, first principles (1P) thermodynamics, and metrics which are measures of inefficiency are well understood.
Software, an analytics app, based on AI is used to automatically quantify efficiency in the cooling towers. This is information useful to optimize the time of cleaning: not to late causing inefficiency, not to early causing unnecessary production downtime. This reduces energy consumption. The exact configuration of sensors depends on the performance metrics you need. The recommendation is to use a specialized cooling tower app with special features for cooling tower analytics. The same app also has analytics to predict mechanical issues as well as for water chemistry.
The transformative nature of the software is the same as described for heat exchangers above. The general software attributes required are also the same as for heat exchangers.
The recommendation is to add quad-temperature, flow, and relative humidity (RH) sensors to take the place of manual rounds to automatically collect the data required by the analytics for accurate and timely quantification. An important point is to use a quad input temperature transmitter for time synchronized temperature differentials (ΔT) between inlets and outlets.
The impact the sensors have is the same as described for heat exchangers above. The required sensor attributes are also the same as for heat exchangers.
Air-Cooled Heat Exchanger Monitoring Solutions
Air-cooled heat exchanger (ACHX) inefficiency is principally fin fouling. The inefficiencies, first principles (1P) thermodynamics, and metrics which are measures of inefficiency are well understood.
Software, an analytics app, based on AI is used to automatically quantify efficiency in the air-cooled heat exchangers. This is information useful to optimize the time of cleaning: not to late causing inefficiency, not to early causing unnecessary production downtime. This reduces energy consumption. The exact configuration of sensors depends on the performance metrics you need. The recommendation is to use a specialized ACHX app with special features for ACHX analytics. The same app also has analytics to predict mechanical issues.
The transformative nature of the software and the general software attributes required are the same as described for heat exchangers above.
The recommendation is to add quad-temperature and flow sensors to take the place of manual rounds to automatically collect the data required by the analytics for robust and timely quantification. An important point is to use a quad input temperature transmitter for time synchronized temperature differentials (ΔT) between inlets and outlets. For ACHX with multiple cells, this is done on a per-cell basis to enable fouling to be pinpointed to a specific cell which can be bypassed and cleaned while the other cells remain in service.
The impact the sensors have, and the required sensor attributes are the same as described for heat exchangers above.
Steam Trap Monitoring Solutions
Steam trap failure modes include blowing steam and trapping condensate. Both are bad as one is direct steam loss and the other may cause inefficiency. Condensate can also cause water hammer and corrosion damage. These failure modes and their cause & effect, the symptoms, which are indicators of failure are well understood.
Software, an analytics app, based on AI is used to automatically detect blow-through, trapping condensate, or flooding in the steam traps. The amount of energy loss and carbon emissions which would result if action were not taken is estimated. Failed steam traps can be replaced sooner, reducing energy loss. The recommendation is to use a specialized steam trap app with special features for steam trap analytics.
The transformative nature of the software and the general software attributes required are the same as described for heat exchangers above.
The recommendation is to add two-in-one acoustic noise and temperature sensors to take the place of manual rounds to automatically collect the data required by the analytics for robust and timely detection.
The impact the sensors have, and the required sensor attributes are the same as described for heat exchangers above.
Pressure Relief Valve (PRV) Monitoring Solutions
Pressure Relief Valve (PRV) are necessary safety devices that on excessive pressure vent product direct to the atmosphere or release it to flare which burns off the product. Either way there will be greenhouse gas (GHG) emissions such as methane or carbon dioxide. Additionally PRV have failure modes including getting stuck open, internal passing when not seating back properly, bellows failure, and rupture disk burst. These failure modes and their cause & effect, the symptoms, which are indicators of failure are well understood.
Software, an analytics app, based on AI is used to automatically and positively detect and timestamp when the PRV is releasing (as it should) and for how long, as well as undesirable stuck open, internal passing (due to not seating back properly after a releasing), bellows failure, and rupture disk burst in the PRVs. Over time this can be significant product loss and emissions. In the case of pilot operated PRVs, sensors estimate the amount of lift. With this information PRVs can be overhauled sooner, reducing losses and GHG emissions, yet only when required, thus avoiding unnecessary production downtime. The recommendation is to use a specialized PRV app with special features for PRV analytics. Additionally, an overpressure release signifies an underlying process problem so the timestamp of the release can be used in troubleshooting the underlying process problems causing the overpressure, thereby further reducing future GHG emissions.
The transformative nature of the software and the general software attributes required are the same as described for heat exchangers above.
The recommendation is to add two-in-one acoustic noise and temperature transmitter as well as pressure and discrete contact sensors to take the place of manual rounds to automatically collect the data required by the analytics for robust and timely detection. The exact configuration of sensors depends on the type of PRV failure modes you want to predict.
The impact the sensors have, and the required sensor attributes are the same as described for heat exchangers above.
Industrial Light Monitoring Solutions
Leaving luminaires (light fixtures) on at full brightness when there is sufficient natural light, or nobody is present is inefficient. Natural light and occupancy change throughout the day.
Software, an analytics app, based on AI is used to automatically analyze motion and light level to optimize lamp brightness. The recommendation is to use a specialized lighting app with special features for lighting analytics.
The transformative nature of the software is the same as described for heat exchangers above. The general software attributes required are also the same as for heat exchangers.
The recommendation is to add illuminance, motion, and lamp power sensors to automatically collect the data required by the analytics to spot opportunities to optimize lamp brightness. By dimming the lights, when possible, the energy consumption is reduced.
The impact the sensors have is the same as described for heat exchangers above. The required sensor attributes are also the same as for heat exchangers.
Emergency Relief Vent Monitoring Solutions
Emergency relief vents are necessary safety devices on storage tanks that on pressure buildup vent product vapors direct to the atmosphere. In the hydrocarbon industries this includes methane which is a potent greenhouse gas (GHG).
The recommendation is to add a proximity switch and discrete transmitter to automatically detect when the emergency relief valve is venting (as it should) for timely notification. By solving the underlying cause, future GHG emissions are reduced.
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The impact the sensors have is the same as described for heat exchangers above. The required sensor attributes are also the same as for heat exchangers.
Thief Hatch Monitoring Solutions
An access hatch on a storage tank emits product vapors into the atmosphere if left open. In the hydrocarbon industries this includes methane which is a potent greenhouse gas (GHG).
The recommendation is to add a proximity switch and discrete transmitter to take the place of manual inspection rounds to automatically detect hatches left open for timely notification. By closing the hatch sooner, GHG emissions are reduced.
The impact the sensors have is the same as described for heat exchangers above. The required sensor attributes are also the same as for heat exchangers.
Methane Leak Monitoring Solutions
Leak is a common failure mode for flanges, pump seals, compressor rod packings, valve stem packings, and many others. This leads to fugitive emissions. In the hydrocarbon industries this includes methane which is a potent greenhouse gas (GHG).
The recommendation is to add combustible gas sensors to take the place of manual rounds with portable testers to automatically detect methane and other combustible gas leaks. With multiple sensors across the plant, leaks are detected sooner and it is possible to closer identify the area where the leak is occurring. With this information, the methane leaks can be stopped sooner thereby reducing GHG emissions.
Energy Management Meter Reading Solutions
To reduce energy overconsumption and losses, it is necessary to pinpoint where it occurs, in a timely manner.
Software, an ISO50001-style energy management information system (EMIS), detect overconsumption and losses through energy and mass balance, and by comparing actual consumption against target consumption based on current operating conditions such as production rate in real-time.
Only metering energy and utility flows for the plant as it comes in across the battery limits or out from compressors and boilers is not sufficient to pinpoint the location of overconsumption and losses. And doing it monthly is not sufficient. An EMIS requires real-time submetering with finer granularity to identify where the problem lies. The recommendation is therefore to deploy flow sensors on every branch, for every energy stream, to take the place of manual meter reading rounds to automatically collect the data required by the EMIS for timely and pinpoint identification. That is, for each plant area, each process unit, and even down to individual pieces of equipment. Energy streams include the basic “WAGES” (Water, Air, Gas, Electricity, and Steam) plus other utilities like hydrogen, oxygen, and nitrogen used in the plant. Existing turbine flow meters with pulse output can be fitted with a totalizing transmitter. But many additional flow meters will have to be deployed. Flow meter types may include differential pressure (wireless) with 'slip-in' orifice plate, vortex, Coriolis (mass), electromagnetic, and clamp-on ultrasonic (non-intrusive) depending on the product and line size. Additional sensors such as pressure for steam enthalpy calculation may also be required. By detecting and pinpointing overconsumption and losses, they can be stopped, thus reducing energy consumption.
The impact the sensors have is the same as described for heat exchangers above. The required sensor attributes are also the same as for heat exchangers.
Operational Excellence in Sustainability
As a result of improved efficiency, emissions, metering, and other monitoring practices enabled by these new automation tools, the plant will see improvements in reduced consumption which brings lower energy and utility cost, smaller carbon footprint and lower carbon tax. Reduced venting and flaring which brings lower energy cost, lower production cost, and reduced fines. Reduced leaks and other losses which brings lower energy and utility cost. Performance-based cleaning and overhaul which brings reduced scheduled downtime, reduced preventive cleaning, and overhaul cost. Autonomous operation which brings reduced inspection cost (transport, outsourced services) and greater productivity.
But, if this additional automation is not deployed, the plant will remain stuck in its old ways, which might be good, but not great. And for a new plant that does not put in the additional automation sets itself up for a reactive energy management culture and sub-par sustainability.
Wireless Sensor Network Requirements
Sensors are critical to improving sustainability and energy management because analytics software without sensors is like a brain without senses. In an existing plant, wireless sensors are the only practical way since laying cable to hundreds or thousands of additional sensors in a running plant is impractical. That is, the I&C team in the plant must deploy wireless sensor network infrastructure if it doesn’t already have it.
Analytics software without sensors is like a brain without senses.
Now, there are many wireless sensor network (WSN) technologies being touted for plants to choose from, but most are not suitable for plant use-cases because when they were created, they were optimized for other use-cases such as on your person and in your home, smart city, or agricultural use-cases etc. And in some cases the radio technology is proprietary. All those WSN technologies therefore lack features that are critical to large-scale operations in process plants with hundreds or thousands of sensors, solving dozens of use-cases, using sensors for dozens of different kinds of measurements. These critical attributes include automatic conversion of sensor measurement data to OPC-UA and Modbus, central sensor configuration, and common sensor diagnostics management.
Be conscious to not inadvertently direct all your attention solely to one type of sensor, solving one use-case at hand, forgetting about capabilities of the WSN, because you might end up with the wrong WSN not suitable for many of the other sensor use-cases around the plant you will have to tackle in the future.
Multi-vendor Interoperability
Sustainability and energy use-cases around the plant require sensors for quad-temperature, differential pressure (including level and flow), pressure, acoustic noise (steam traps and relief valves), contact, pulse (turbine flow), and wireless adapter (on non-wireless flow meters). Other use-cases for occupational health, safety, and the environment (HS&E), as well as reliability, maintenance, and integrity also require sensors for vibration, ultrasonic thickness (corrosion and erosion), guided wave radar (level), level switch, gas concentration (H2S, CO, and O2). Managing multiple WSN in a plant would be a lot of work. The recommendation is to use a single WSN to support all these sensor types. Therefore, when you select a WSN technology for your plant, make sure you can find local vendors for all these sensor types, not just the type of sensor you require for the project at hand. Keep in mind that wireless sensors need national approvals (e.g. radio spectrum), so although a vendor have a particular type of sensor on their website, you need to make sure it has the approvals for use in your country.
The recommendation is to deploy WSN infrastructure based on the WirelessHART (IEC62591) standard. This standard supports sensors from multiple vendors covering all these sensor types.
Automatic conversion to OPC-UA and Modbus
Many vendors offer a bundled kit of wireless sensor plus an app for a specific use-case. For instance, many vendors offer an acoustic noise sensor with a steam trap or PRV app. At a first glance this may look as if it solves the problem at hand, but in the larger scheme of a plant, such point solutions are not practical because the sensor data is trapped within the bundled app. To improve how work is done in the plant, sensor measurement data must also connect to third-party software apps like the historian, analytics, energy management software, visualization, and maybe even the control system. Therefore the sensor measurement data must be made available using standard industrial protocols and software interfaces. For most WSN that would require custom coding/programming to a non-standard API or scripting to parse vendor-specific data formats. A system integrator has the programming/scripting skills to setup the system, but it is time consuming and costly, so it usually does not get done. But the worst part is that plant personnel will not be able to support it long-term. When OS and software is patched and upgraded, APIs will break. When devices are replaced, different data parsing is required. The system integrator needs to be called in to fix the code or script when a sensor is replaced. That is, most WSN technologies do not provide simple data integration even though the radio may be based on a standard, or the protocol may be a standard.
The recommendation is to deploy WSN infrastructure based on the WirelessHART (IEC62591) standard. This technology uses well-defined data formats which enables automatic conversion of sensor measurement data to OPC Classic (OPC-DA), OPC-UA, Modbus/RTU, Modbus/TCP, HART-IP, and other standard industrial protocols in the wireless gateway. That is, custom coding/programming or scripting is not required. Existing and future systems and software in the plant can easily tap into the sensor measurement data. IP-based protocols like OPC-UA, Modbus/TCP, and HART-IP can run all the way to software in the cloud if required. Sensors can be replaced without calling in a system integrator. The risk associated with software patching and upgrade is greatly reduced.
Central sensor configuration
Most WSN only support transmission (publication) of sensor measurement value and simple status. The sensor cannot receive configuration data over the WSN. That is, changing the sensor configuration (such as type of temperature sensor or engineering unit) requires you to travel to site to change the configuration locally at the sensor, which is inconvenient in any plant, but particularly troublesome for offshore installations and other remote sites.
The recommendation is to deploy WSN infrastructure based on the WirelessHART (IEC62591) standard. This technology supports both reading and writing of data from a remote location making it possible to centrally configure sensors from configuration software or an intelligent device management (IDM) system. Moreover, gateways support the HART-IP protocol which seamlessly carries the data between the gateway and software such that additional integration effort is not required to setup such capability. Sensors from multiple vendors can be configured from the same software. Sensors can therefore be configured without going to site.
Common sensor diagnostics management
Again, most vendors offer a bundled kit of wireless sensor plus an app, and the health of the sensor, its diagnostics, is only displayed in that app. Once again, at a first glance, this may look as if it solves the problem at hand, but in the larger scheme of a plant, such point solutions are not practical because with the wide selection of sensor types required in a plant, the I&C engineers that support the sensor system must open multiple apps, possibly on multiple computers, in different locations, to see the health of all sensors. This would not be practical.
The recommendation is to deploy WSN infrastructure based on the WirelessHART (IEC62591) standard. This technology uses well-defined data formats which enables a single common software to display the health of sensors, and status of their individual measurements, from multiple vendors. No need to wade through multiple apps. Moreover, gateways support the HART-IP protocol which seamlessly carries the data between the gateway and software such that additional integration effort is not required to setup such capability. The health of sensors from multiple vendors can be monitored from the same software.
Action Plan: Transformation by Wireless Automation
Transformation of sustainability and energy management requires automation such as wireless sensors and specialized analytics apps. Therefore the recommendation is for companies to assign larger budget to their I&C departments to enable them to deploy the automation required to transform how work is done.
For existing plants:
For engineering specs:
Wireless sensor network gateway:
Wireless sensors:
Intelligent Device Management (IDM) software:
For new projects:
Lead the way. Schedule a meeting for 22nd April, Earth Day, or today.
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And remember, always ask vendor for product data sheet to make sure the software is proven, and pay close attention to software screen captures in it to see if it does what is promised without expensive customization. Well, that’s my personal opinion. If you are interested in digital transformation in the process industries click “Follow” by my photo to not miss future updates. Click “Like” if you found this useful to you and to make sure you keep receiving updates in your feed and “Repost” if you think it would be useful to others. Save the link in case you need to refer in the future.
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