High-Fidelity battery modeling.

26/05/2015
The use of virtual battery technology in the design of test systems can facilitate the development of better products, reduce project risks, and get products to market faster.

The use of rechargeable batteries in consumer products, business applications and industrial systems continues to grow substantially. The global market for all batteries will reach almost €68 billion (US$74b) this year, and rechargeable batteries will account for nearly 82% of that, or €55 billion (US$60b), according to market researcher Frost & Sullivan.

Figure 1: Simulation of thermal runaway using the Li-Ion model from the MapleSim Battery Library

Growth like this means several things. First, large companies have moved or are moving into the market, designing and offering products ranging from hand-held devices to large power back-up systems. Second, as the systems get larger, battery technologies have to match the technical challenges of increasing cell capacity, thermal stability, life extension and disposal.

Meeting the Technical Challenges

Monitoring and controlling larger cell arrays through Battery Management Systems (BMS) helps to minimize charge times and maximize efficiency and battery life. Design and testing of a sophisticated BMS can pose challenges, however, as was discovered by one of the largest producers of electronic products in the world. That’s why they recently relied upon Maplesoft and ControlWorks Inc., a real-time testing systems integrator with deep experience developing BMS test stands, to develop a Hardware-in-the-Loop (HIL) test system for the BMS in one of their large  Energy Storage System (ESS) products.

An attractive solution to these testing challenges is to use virtual batteries – mathematical models of battery cells that are capable of displaying the same dynamic behavior as real ones – for early-stage testing of the BMS. Not only have these models proven to be highly accurate, they are computationally efficient and are able to achieve the execution required to deliver real-time performance for batteries containing hundreds of cells on real-time platforms.

The battery modeling technique employed by Maplesoft uses a partial differential equation (PDE) discretization technique to streamline the model to a set of ordinary differential equations (ODE) that can be readily solved by system-level tools like MapleSim. The advanced model optimization features of MapleSim also allow the resulting code to be very fast and capable of running in real-time.

The resulting battery models can also be employed in the prediction of charge/discharge rates, state of charge (SoC), heat generation and state of health (SoH) through a wide range of loading cycles within complex, multi-domain system models. This approach provides the performance needed for system-level studies with minimal loss in model fidelity. The user can also allow for energy loss through heat, making these models useful for performing thermal studies to determine component sizes in cooling systems to manage battery temperature. Not carefully controlling the temperature can lead to reduced operational life or, in extreme cases, destruction or even explosion due to thermal runaway, a common problem in many battery-powered systems.

Model Structure for this Application
For the purpose of this ESS test system development project, the key requirements for the battery model were:

-Up to 144 Li-Ion polymer cells for testing the BMS of the client’s ESS products
-Ease of configuration for different requirements (parallel/series networks)
-Several sensors per cell (current, voltage, SoC, SoH)
-Variation of chemistry make-up due to manufacturing tolerances
-Fault-insertion on each cell (open-circuit, shorting)
-Capacity to run in real-time (target execution-time budget of 1 ms)
-In the case of energy storage systems, like this example, each ESS battery is made of several “stacks” that, in turn, contain several cells. The MapleSim model follows this structure with each cell being a shared, fully parameterized subsystem. Each cell can also be switched to open circuit using logical parameters.

Figure 2: Cell stack model

The stack model is made of 18 cell subsystems connected either in parallel or series, depending on the requirement. Input signals are provided for charge balancing from the BMS. Output signals are provided back to the BMS to monitor the condition of the stack (supply voltage, SoC and SoH). Finally, the full ESS is made of several stacks with IO signals fed to and from the BMS.

Figure 3: ESS Battery model

Model Calibration and Validation
Much of the accuracy of this model is dependent on experimentally derived parameters, determined from charge/discharge test results. Project engineers determined that any deviation in performance due to manufacturing variations needed to be included in order to test the charge-balancing capability of the BMS. Instead of testing every cell, engineers relied on random variants generated from the statistical distribution determined by the charge/discharge test results on 48 cells. This was applied to all 144 cells and then compared with the real test results. The maximum variance of the voltage from the experimental data was 14mV, while from the simulation it was 13mV, acceptable for the purpose of this project.
Maplesoft and ControlWorks Inc. engineers also determined the average cell response using the parameter-estimation tool supplied with the MapleSim Battery Library. This uses optimization techniques to determine the values of cell-response parameters that provide the closest “fit” to the experimental results. This response was then validated against response data from other cells to ensure close estimation of the resulting model.

SoH behavior was implemented as a look-up table based on experimental results. The model determines the capacity and internal resistance based on the number of charge/discharge cycles and depth of discharge (DOD) from the lookup.

Figure 4: SoH simulation showing effect on battery voltage

Finally, the model was converted to ANSI-C through the MapleSim Connector, producing an S-Function of the battery model that can be tested for performance and accuracy with a fixed-step solver on a desktop computer in MATLAB/Simulink® before moving it to a real-time platform. The simplest solver was used and the performance bench showed that the average execution time was approximately 20 times faster than real-time, occupying 5.5% of the real-time system time budget. This shows that the battery model can be easily scaled up, if required.

The end result was a battery model capable of being configured to represent a stack of up to 144 cells that can be connected in any combination of parallel and series networks. Fault modes were also built-in, such as individual cells shorting or opening, as well as incorporating variations in charge capacity from cell to cell, and degradation of capacity over the life of the cells.

The final BMS test station provides the client’s engineers with the ability to configure the battery model (number of cells, series/parallel, etc.) and apply a range of tests to it. The engineer can go back to the MapleSim™ model at any time to make any necessary changes to the model configuration, and then generate the model for use on the real-time platform. In this system, the real-time software is National Instruments’ VeriStand™, driving a PXI real-time system. The MapleSim Connector for NI VeriStand™ automates the model integration process, allowing the engineer to produce the real-time model quickly and reliably.

The ControlWorks Inc. system also integrates real-time platform, signal processing, fault-insertion tools and standard communications protocols (CANbus for automotive, Modbus for industrial applications), allowing the engineer to run the BMS through a range of tests on the battery model, including Constant Current (CC) and Constant Voltage (CC/CV) charge/discharge cycles, as well as Constant Power (CP) and Constant Resistance (CR) discharge cycles.

“We were pleased to be able to partner with Maplesoft on this project,” said Kenny Lee, PhD, Director of Research Center of Automotive Electronics, ControlWorks Inc. “The use of battery models in this case proved to be an effective alternative to the use of real batteries,” he added.

Summary
Test automation and simulation is critical in system-level testing, allowing time and cost of failure analysis, constant development pressure, expense of repeated tests, and lengthy set-up times all to be addressed.

“The use of high-fidelity, ready-made battery models allows the engineer to avoid the risks of damage to batteries, along with subsequent costs, while testing and optimizing the BMS design in a close-to-reality loading environment,” said Paul Goossens, Maplesoft VP of Engineering Solutions.
The use of virtual battery technology in the design of test systems can facilitate the development of better products, reduce project risks, and get products to market faster.

“The MapleSim model of the Li-Ion battery was selected because of its proven ability to achieve real-time performance. The code-generation and compilation tools are very easy to use, making the integration of the model into the HIL system very fast and cost-effective. That, plus the excellent development support we received from Maplesoft’s Engineering Solutions team made this a very smooth project.”  Kenny Lee, PhD, Director of Research Center of Automotive Electronics, ControlWorks Inc.


ABB Process instrumentation, analytical technology and gas detection in Ireland

19/01/2015

Hanley Measurement & Control has built a reputation for the supply of specialist solutions and expertise in process instrumentation, process analytical technology and gas detection. Founded in 1981 it has long been considered as a leading automation in Ireland. The company has recently been appointed as channel partner in Ireland by ABB, to expand its instrument and analyser offering into the Irish process market

Left to Right: Chris Kennedy, Gavin O’Driscoll & Eoin O’Neill of Hanley Measurement & Control together with Aidan Edwards of ABB stand next to a representation of a 3 meter magnetic flowmeter (the largest every supplied!) during a recent visit to the ABB flow meter manufacturing facility in Stonehouse, GB.

Left to Right: Chris Kennedy, Gavin O’Driscoll & Eoin O’Neill of Hanley Measurement & Control together with Aidan Edwards of ABB stand next to a representation of a 2.4 meter magnetic flowmeter (the largest every supplied!) during a recent visit to the ABB flow meter manufacturing facility in Stonehouse, GB.

The partnership will see the company acting as the official sales agent for ABB’s complete portfolio of instrumentation and analyser products for applications in the pharmaceutical, chemical, food and beverage and other related industries.

Chris Kennedy, Managing Director of Hanley Measurement & Control commented that “partnering with ABB enables the company to provide its customers with an enhance product range specifically in relation to flow measurement and analytical solutions.”

Commenting on the partnership, Tim Door, General Manager for ABB’s Measurement and Analytics business in the Britain and Ireland says: “The partnership with Hanley Measurement and Control marks a positive move forward that underlines our intent to grow our presence in the Irish process market. The company is a great fit for our growing range of measurement and control products for improving process performance and efficiency.”

“Utilising a well-known and respected partner such as Hanley Measurement & Control will allow our customers in Ireland to get full access to support and service going forward into 2015 and beyond.”

• Following the completion of a management buyout Hanley Measurement & Control is no longer part of the Hanley group of companies. Hanley Measurement & Control is now a subsidiary of Eolas Scientific which also has an operating company in the UK called Eolas Technology. The management team of Chris Kennedy, Gavin O’Driscoll and Eoin O’Neill are committed to ensuring our customers receive exceptional service and a world class range of products.

Increased yields can help drive emobility uptake!

24/07/2013
The benefits of renewable energy sources such as water, wind and solar power are well known, and electric cars are not a new concept.  However, to reduce our dependence on fossil fuels, which will be exhausted in just one century, efficient ways of mass producing the cells need to be established.

The electric motor can be up to four times more efficient than the combustion engine.  Given the finite nature of fossil fuels, the uptake of electric cars or emobility as it is now called is inevitable.  However, what does this mean for automation technology?  Steve Sands, Product Manager at Festo GB, reviews the current situation.

eleccarFossil fuels are expected to be depleted by 2112 and a report by WWF says that in Great Britain one in 17 cars by 2020, and one in six by 2030, must be electric if they are  to meet emission targets and bring an end to a dependence on oil.  This means that 1.7 million electric vehicles  will need to be put on the road by 2020 and then treble take-up of the technology in the following decade if it is to meet its climate change targets.

However, there were only 1,052 claims on the British government electric car subsidy in 2011 and one of the barriers to the adoption of electric vehicles is their cost.  Despite government grants the vehicles are still expensive, constrained in supply and there are too many good conventional alternatives.

For electric cars to compete against Britain’s fleet of 31m fossil fuel cars they need to be more affordable.  Consumers will only accept emobility suitable for daily use when the batteries allow a sufficient distance to be travelled and their cost does not increase the vehicle price compared with an equivalent petrol or diesel model.

Expensive batteries
Currently between 30 and 40 per cent of the cost of electric vehicles is down to the battery.  Battery production manufacturing costs are high and there is a lot of research into alternative materials and processes.  For now, one way to reduce cell costs is through increasing efficiency in automation.

The problem is that battery lithium-ion production is still predominately a manual process with a large number of individual steps.   During the battery manufacturing process, two battery cells are bonded by a foil.  Each cell is bonded to a copper plate and several double cells are combined to form a battery pack.  With conventional automation processes the cells can only be gripped in certain places using vacuum generators. This means that reliable holding of the cell is not guaranteed.

The handling of sensitive lithium-ion cells is a major challenge for battery manufacturers as the cells can be easily damaged or contaminated during the manufacturing process.  If we are to eventually have efficient mass production, we need to set up technologically flexible electrode and cell production for the manufacture of battery prototypes with a high degree of standardisation and automation.  Mechatronic solutions which integrate expertise from different areas of process and factory automation and transfer it to the latest technologies in battery production look promising.

Air bearings as a solution
ads-tec, a company which develops automated production systems for high performance lithium energy storage devices first developed a production method for automating the bonding, feeding and handling of cells on a laboratory scale.  The aim was to provide production facilities that would allow the fast, low cost production of cells and battery systems.

festoemobilityEngineers worked with Festo to develop a new front-end solution for handling lithium-ion cells, with an air bearing.  The ATBT air bearing was initially designed for use in the solar or electrical manufacturing industry.  It produces an evenly distributed layer of air on its fine surface, which allows delicate objects to glide smoothly, enabling the reliable contactless transport of sheets of glass and delicate film.

Festo’s experts have now applied the technology to battery production, by making use of a reverse effect.  Instead of ‘blowing’ air, the air bearing draws a vacuum which is distributed over the large surface.  Their ATBT suction pad has been used in a battery bonding prototype machine, where it is ideal for handling, clamping and holding the delicate cell packs during production

FT01354As the new air bearing grips the entire surface of the battery plate, the bonding process is no longer prone to stress fractures caused during production.  It is proving to be an effective solution for efficient automation processes and increasing productivity.

Despite their higher costs the level of acceptance for electric cars is growing worldwide.  This applies in particular to the emerging markets of China and India, where mobility is also rising.  92% of people in India and 88% in China are willing to consider an electric car if buying a new car within the next five years.  In Britain and France this figure is only 57%, but the number is rising and production innovation will help reduce costs and bring forward the emobility revolution!


Oil & Gas to power Industrial valves & actuators

10/04/2013
Political instability in key growth regions threatens to dampen revenue generation

valvecontrolThe oil and gas industry is the largest revenue generator for the industrial valves and actuators market, globally. As a result, the exploration of new oil and gas fields, with the corresponding increase in investments in refineries and pipelines in key growth regions, is likely to have a high impact on market prospects. The market will also benefit from the rising demand for automation and infrastructure modernisation.

New analysis from Frost & Sullivan, Strategic Analysis of the Global Industrial Valves and Actuators Market, finds that the market earned revenues of €14.23 billion ($18.37b) in 2012 (€13.67 0r $17.65 billion in 2011 the base year for the report) and estimates this to reach #17.12 billion ($ 22.10b) in 2016.

“Currently, significant oil exploration activity is taking place in Africa, South America, the Middle East and Russia,” noted Frost & Sullivan Industrial Automation and Process Control Research Associate Niranjan Paul. “These regions will, therefore, be the focus of industrial valve and actuator manufacturers and provide sustainable growth opportunities.”

In the Middle East, Iraq is emerging as a prime market for major oil companies. The country is also projected to spend nearly €20.91 billion ($27.00b) on new power generation, distribution and transmission projects between 2012 and 2017. These trends mark out Iraq as a significant market for industrial valves and actuators.

However, political uncertainty in North Africa and the Middle East is affecting the oil and gas industry. This, together with sanctions imposed on Iran and Syria – major markets for the oil and gas and power generation industries – has the potential to dampen revenues of industrial valve and actuators manufacturers.

In the more developed regions of North America and Europe, environmental legislations are playing an important role in shaping the course of the market. Here, stringent regulations are likely to have an impact across end-user industries.

“In the oil and gas industry, regulations are in place to reduce air pollution by targeting a reduction of smog forming volatile organic compounds (VOC) emissions,” explained Paul. “The design of process equipment such as valves and actuators will be instrumental in achieving lowered plant emissions.”

Even as major industrial valves and actuators manufacturers look to leverage growth opportunities, their profit margins are being squeezed by high price sensitivity across end-user industries. As a result, companies are being forced to adopt aggressive pricing strategies.

“The market is likely to experience increased demand for custom solutions that suit particular end-user industry applications,” concluded Paul. “Market participants need to meet such needs and also address other key customer requirements such as high-quality after-sales service and shorter delivery time.”


Pressure Tx demand boosted in Asia

15/03/2013
Escalating energy demand  to  boost Asia’s pressure transmitter market

Pressure transmitter shipments, after returning to pre-recession levels in 2010, saw strong growth in 2011, even surpassing 2010’s growth by several percentage points. Despite the return to positive growth, risks remain. In a connected world, market volatility and political instability impact industrial growth, which in turn affects the pressure transmitter business. The relative slowdown in China and India further increases the risks facing pressure transmitter vendors. Due to the numerous challenges and the lag between orders and final shipment for new projects, many vendors are weighing the financial viability before investing in their manufacturing capacity and after-market service capabilities.

tempTXAsiaAccording to G. Ganapathiraman, Country Manager, ARC India and co-author of ARC’s study titled Pressure Transmitters for Asia Market Research Study  , “Ongoing financial instability continues to plague markets in Asia. However, ARC expects the developing countries such as China and India to drive the majority of Asia’s growth in pressure transmitter shipments going forward.” Countries such as Indonesia and South Korea are also growing at above average growth rates. Despite the country’s slower GDP growth in recent quarters, the electric power industry in China continues its investments, creating opportunities for pressure transmitter suppliers. In India, the need for both increased generating capacity and a more stable power infrastructure to reduce brownouts drives the need for more pressure transmitters.

Market Potential for Pressure Transmitter Suppliers
The oil & gas, electric power, chemicals, and metals & mining industries are leading for the deployment of pressure transmitters. Among these, the metals & mining industry is experiencing above average growth. Asia’s ongoing demand for energy will require more oil and gas, driving demand and long-term energy prices. Finding new oil deposits is becoming more difficult and those that are found tend to be more challenging to develop. Many of these new oil fields produce heavy oil rather than lighter sweet crude oil. The high sulfur content increases refining costs and requires different refining methods. This will spawn investment in new refineries capable of refining the heavy oil deposits in the Asia Pacific region. At the same time, environmental concerns are increasing, creating significant opportunities for pressure transmitter suppliers in the oil & gas industry.

Smart and SIL-rated Transmitters See Big Gains
Sales of smart pressure transmitters will continue to outpace those of conventional and low-cost devices, as users seek to utilize recent technological advances to improve visibility into plant operations to help maximize productivity and the availability of production resources. This user focus on asset management also fuels demand for transmitters that incorporate onboard diagnostics capabilities and use digital communication protocols.

Increasingly tough safety and environmental regulations have helped drive increasing adoption of safety integrity level (SIL)-rated transmitters for safety instrumented systems (SIS) to mitigate the risk of catastrophic events. Pressure transmitters are an integral part of safety instrumented systems, and most leading pressure transmitter suppliers now offer SIL-rated transmitters.


Advanced metering market

24/01/2013
Advanced Metering Infrastructure Market to Grow at Fast Rate, States Frost & Sullivan. Revenues expected to triple – Massive opportunities for communication systems and network, meter data management (MDM), customer and programme data management

SmartMetersLegislation and standardisation are set to catalyse the advanced metering infrastructure (AMI) market in Europe. Market participants are working towards standardisation and fulfilling regulatory requirements for the development of smart meters and AMI to begin mass rollouts. In Ireland we are faced with metering of domestic water supply in the short term future for instance.

New analysis from Frost & SullivanEuropean Advanced Metering Infrastructure (AMI) Market, finds that the AMI revenue in Europe is expected to grow from €85m ($1.13b) in 2011 to €2.8b ($3.72b) in 2016 at a compound annual growth rate (CAGR) of 26.9%. The research covers smart meters, installation, communication systems and network, meter data management (MDM) and customer and programme data management.

“Emerging smart grid technologies, which support enhanced energy management, will boost the installation of AMI in Europe,” noted Frost & Sullivan Energy & Power Supplies Research Analyst Neha Vikash. “The market is expected to witness higher growth not only in smart meters and the installation segments, but also in communications networks, MDM, customer and programme data management segments as well.” Most companies in the AMI space are not just the hardware (meter) providers. They combine them with important services and appropriate functionalities in communication infrastructure and data management. These are the key technologies for the deployment of innovative solutions. Installation of hardware does not generate a constant stream of revenue.

Despite its obvious benefits, smart meter implementation reveals regional disparities. Market growth has been faster in Western and Northern Europe. The lack of regulatory drive and utility implementations has affected installation rates in Central and Eastern Europe. It is expected that the smart metering activity in the CEE region will follow the Western European knowledge wave and experience. “It is also expected that once large scale roll out activity begins in Central and Eastern Europe, the pace of implementation will be faster compared to that of Western Europe,” concluded Vikash. “Regulatory approval, along with increased competition, aging infrastructure, and new technology will continue to drive investments in advanced metering and intelligent grid technologies.”

Nevertheless, EU member states that lack the regulatory push for deployment will experience large-scale implementation after 2015, as they have to comply with the EU’s Third Energy Directive, or pay a high penalty fee.

“AMI is an important step towards achieving the EU 20-20-20 goal which states that by 2020, 80 per cent of households must have smart meters and complete rollout achieved by 2022,” elaborated Vikash. “Government mandates will, therefore, be a key driver for AMI deployment.”

In addition to legislation, the lack of communication standards and security issues also play a major role in determining market prospects. In fact, data security is an issue among all member states, but it is of higher importance in the UK, Germany and the Netherlands. This has resulted in a delay in smart meter roll out plans by utilities.

“Standardisation is likely to affect future smart meter sales, development and innovation,” concluded Vikash. “Meters complying with security requirements as per the standardisation mandate as well as satisfying regional legislative security requirements are likely to encourage customers to adopt smart meters.”


W.A.G.E.S. for cost reduction!

07/01/2013

This paper from Endress + Hauser, discusses the increase in understanding and necessity of monitoring and controlling energy efficiency in utilities.

1. Introduction
Production plants in all industries are coming more and more under pressure to measure the cost of their utilities:

– Water
– Air
– Gas (e.g. Natural Gas, other gases or fuels)
– Electricity and
– Steam

It is interesting to confirm that this W.A.G.E.S. trend is independent of the type of industry. It is to be seen in small breweries and in big chemical sites.

One important driver for this pressure is the rise in the cost of energy. The cost of natural gas for industrial applications has more than tripled within less than ten years and the price for electricity in Europe has risen by 30% within less than 4 years.

Certifications according to EMAS and the ISO 14000 series also force customers to measure the energy streams using calibrated technology.

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The utilities have been neglected frequently in the past. Currently, however, they are coming more and more into the focus. Still many companies only measure natural gas and electricity only at the custody transfer point. Using these few measurements, however, important parameters like specific energy consumptions are determined that give important indications: how much energy does it take to make a ton of product? These measurements, however, are only taken on a monthly or sometimes even on a yearly basis. Investing a relatively small amount of money in comparative terms it is possible to set up energy monitoring systems that measure the consumption of each respective utility close to the point of use. These measurements can then be used to build meaningful relations between energy consumptions and driving factors that enable the customer to

• Control their energy consumption with a better resolution (application-wise and time-wise)
• Identify and justify energy reduction projects (where is most energy consumed? Which changes are possible?)
• Detect poor performance earlier (are the boiler’s heating surfaces fouling?)
• Get support for decision making (should the contract with the provider of electricity be changed?)
• Report performance automatically (which Energy Accountability Centre/shift etc. is performing best? Did exceptions occur?)
• Audit historical operations
• Get evidence of success (did promises made by a manufacturer of energy efficient equipment come true?)
• Get support for energy budgeting and management accounting
• Provide the energy data to other systems (e.g. existing SCADA)

2. What is energy management

Picture 1: The Energy Management Cycle.

Picture 1: The Energy Management Cycle.

Energy management can be seen as a cyclic operation. Everything starts with the basic data collection: energy consumption is measured and converted to appropriate units. For most of the utilities, these conversions require highest attention:

– already the conversion from volumetric units (e.g. natural gas measured by turbines, steam measured by DP devices or vortex meters) to corrected volume, mass or energy often is done in a wrong way resulting in errors in the range of typically 10…30%
– many devices are wrong installed resulting in similar error ranges and
– if already the basic is wrong, the analysis will be wrong and all action taken will be based on wrong information.

The easiest form data collection is paper and pencil. It is amazing to see how many people in the industry still have to walk around the factory and find certain meters on a monthly basis to take the readings. Modern systems perform this automatically: Modern recorders as stand-alone devices or so-called “software recorders”are able to record data in the commonly used 15 min. or 30 min. intervals. If these intervals are not sufficient, even a data collection every 100ms is possible.
Most modern systems of data collection are even able to collect the data of up to 30 devices using bus communication and pass the data on using “Field Gates”.

3. Data analysis
If the Data collection is the basis of it all, data analysis is the heart: It helps to convert the pure measurements of energy data into meaningful data.

A first basic way consists in analyzing the 15-min or 30-min data profiles:

– What is the base-load of the application? Why energy is still consumed without production? How can this base-load be reduced?
– What is the typical maximum load during productive hours? How can the maximum load be reduced? (This is important e.g. for electricity contracts)
– What is the typical load distribution? How can a more uniform load-distribution be obtained?

For this purpose, different policies of load-management are available (e.g. peak-clipping)
Even more meaningful is to put energy consumptions into relation to a driving factor. Examples are:

– how much heating energy is consumed compared to how cold the weather is (so-called degree days)
– how much energy is consumed to make a ton of product
– how much electricity is consumed in order to light a building compared to the hours of day-light.

Since all of these parameters put into relationship energy consumption with a relevant driver, they are generally called “Specific Energy Consumptions” (SEC).

Controlling such a factor now enables the customer to control if a certain process is drifting over time, i.e. the process is becoming more in-efficient. Possible causes of such a drift can have multiple reasons:

– the amount of leakage in a compressed air grid is growing because of lacking maintenance
– the specific energy consumption for making steam is rising because of lacking maintenance of steam traps (steam traps fail open in case of a failure)
– the specific energy consumption for heating a building rises because of fouling of the surfaces of heat-exchangers

Generally, comparing the energy consumption with a driver will reveal a linear relationship. In certain applications, this linear relationship also shows an intercept that does not equal zero.

If no actions are taken, the trend will be as follows:

– the intercept grows (examples: increasing leakage in a compressed air application or due to failing steam traps)
– the slope of the linear relationship grows (loss of efficiency e.g. because of fouling heat-exchangers)

Customers, however, will strive to

– reduce the intercept and
– reduce the slope of the linear relationship.

The linear relationship found can now be used as a target for the future. One example: if in the past it has taken 4 GJ of energy to make a ton of steam, we expect this same value for the future, too – unless we take any actions to improve efficiency.

We can now compare the real energy consumption to the expected one and record the differences. If this difference exceeds a certain value, a warning will be generated.

pic2

Picture 2: The Control Graph for controlling deviations from a pre-set target. If the control limits are exceeded, an alarm can be generated

We can also take these differences and total them up over time in the so-called CUSUM (cumulated sums) chart.

Picture 3: The CUSUM chart. It acts as a totalizer and can reveal savings achieved.

Picture 3: The CUSUM chart. It acts as a totalizer and can reveal savings achieved.

This chart acts like a bank-account: If the process becomes less efficient, the CUSUM chart will run away from the zero line. In the picture the process has become more efficient, however. In our example, an economizer was installed improving a steam boiler’s efficiency. We can now read directly from the chart that compared to former performance the investment into the economizer saved the company 1100 MWh of energy within 15 weeks.

Where this data analysis can be done?
Recording the performance, analyzing data every 15 or 30 minutes and displaying current specific energy consumption values can be done easily using modern time recorders that display these values close to the process. These modern recorders already can perform even complex math operations. Thus, employees running certain processes can be directly involved and start asking questions:

– Why are certain shifts more efficient than other?
– Why was the specific energy consumption stable for months but started drifting recently?

These analysis techniques and also the “targeting” procedure described above can also be performed in Energy Monitoring software.

pic4

Picture 4: Set-up of a typical full-blown energy monitoring information system

4. Communication/reporting
Recipients of Energy reports can be found in different hierarchies: from operations personnel to top management and in different areas of a company (production/operation/engineering, controlling, energy and eco management).

The reports must provide information to enable the user to act. Operational staff needs to know when a problem has occurred as quickly as possible and know what they should do about it. Senior management, on the other hand, needs summary information to know that procedures and systems are working well. In order to design reports, it is important to understand who needs reports and why.

Reports to senior management might include:

– a summary of last year’s costs, broken down into EACs (energy accountable centers)
– a summary of the current year’s performance on a monthly basis

• against budget
• against the previous year
• against targets

– a note of the savings (or losses) achieved to date and how they were achieved
– a note of additional savings opportunities and what actions are ongoing to address them

A new report to management should be issued each month and be available in time for board meetings.

Operations management will be responsible for operating processes and plant efficiency. They will need to know on a shift, daily, weekly or monthly basis (depending on the nature of the process and the level of energy use) what energy has been used and how this compares with various targets. The information will be used to

– measure and manage the effectiveness of operations personnel and process plant and systems
– identify problem areas quickly
– provide a basis for performance reporting (to executives)

Operations personnel need to know when a problem has occurred and what needs to be done to rectify it. This information needs to be provided in a timely manner, which might mean within a few minutes of the event for a major energy-using process, or within a day or a week.

Engineers associated with operations will need reports similar to those for operations personnel. Engineers may typically be involved with problems where there is more time to act (compared with process operators), for example, cleaning heat exchangers, solving a control problem or removing air from a refrigeration condenser.

Engineers who are not directly in operations but who provide support will need more detailed historical information. Typically, these individuals will be involved in analyzing historical performance, developing targets and modeling. They will require access to the plant data historian and will use analysis tools, ranging from commonly available spreadsheet software to advanced data mining and similar software.

Engineers that are involved in projects will need supporting data, for example, levels of energy use, process operating conditions, etc. They will also need access to the raw data in the historian and access to analysis tools.

The accounts department may be interested in actual energy usages and costs to compare with budgets. They will need information that is broken down by department so that costs can be allocated to related activities. Accurate costing of operations and the cost of producing goods can improve decisions regarding product pricing, for example, and the allocation of resources.

Energy and environmental managers will need summary data that identifies the performance achieved and trends, much like what executives and operations managers require. Like engineers, they may require more detailed information for specific analysis.

The environmental department may want energy consumption expressed as equivalent CO2 emissions, and the energy reports may need to be integrated into environmental reports that are more general. Summary information may be required for annual energy and environmental reporting and may be needed more frequently by regulatory bodies.

The energy manager may be involved in energy purchasing as well as efficiency. He may need information about the profile of energy use (using a half-hourly graph, for example), peak usage, nighttime usage, etc. The energy manager will also need access to the raw data in order to allow evaluation of purchasing options and to check bills.

We can see from this broad variety of requirements that modern Energy Management Information Systems have to be very flexible in creating these reports.

5. Taking the action
Results of implementing Energy Monitoring Informations Systems in the UK indicate that, when properly implemented, such a system can save 5 to 15 percent of annual energy costs. As an initial approximation, 8 percent appears to be a reasonable estimate. [1]

Implementing an Energy Management Information System alone and taking the action based on the outcome of this tool alone will result typically in 8 percent savings. Most experience regarding this tooling can be found in the UK based on the local “Carbon Trust”.
Further savings can be achieved by spending capital cost e.g. for more efficient burners and boilers, economizers etc.

Savings Strategies in Energy Management typically fall into the four following categories:

• Eliminate. Generally, one should question if certain processes or sections of a plant are really required or if they could be replaced. A simple example: eliminating dead legs of a plant.
• Combine. CHP is a well-known “combine” process: generation of heat and electricity are combined. Another example is the use of off-heat created by compressors for making air e.g. for pre-heating factory air.
• Change equipment, person, place, or sequence. Equipment changes can offer substantial energy savings as the newer equipment may be more energy efficient. Changing persons, place, or sequences can offer energy savings as the person may be more skillful, the place more appropriate, and the sequence better in terms of energy consumption. For example, bringing rework back to the person with the skill and to the place with the correct equipment can save energy.
• Improve. Most energy management work today involves improvement in how energy is used in the process because the capital expenditure required is often minimized. Examples include reducing excess air for combustion to a minimum, reducing temperatures to the minimum required. Improving does sometimes require large amounts of capital. For example, insulation improvements can be expensive, but energy savings can be large, and there can be improved product quality.