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Opinion column by Vasily Tarasovsky, Director for Energy Efficiency, GCE Group, and Ilya Petukhov, Head of the Department of Scientific Research, GCE Group, exclusively for SmartGrid.ru. - GCE Group

Today, the government requires the calculation the norms for specific consumption of fuel and energy resources (FER) only in some cases, defined, for example, in the Order of the Ministry of Energy of the Russian Federation of December 30, 2008 No. 325 On the organization of the Ministry of Energy of the Russian Federation, according to standards of technical losses in the transfer of thermal energy. However, the norms for specific consumption of FER represent an energy efficiency index that serves as a tool to determine energy saving potential which can be realized via organizational measures, equipment repairs, its modernization or replacement, adjustments of operating regime, etc. For this reason, the calculation of the norms for specific consumption of FER is an important task for energy auditors and the companies.

Calculating the indicator of energy efficiency for an energy-consuming facility shows how effectively this facility operates. At first glance, everything is rather simple – we divide the amount of incoming energy by a certain quantity of the product and obtain the specific energy consumption (SEC). Even for complex energy-consuming facilities we can determine SEC by using the measuring equipment while taking into account all the pathways of energy resources. But this way we only get the actual value of the specific energy consumption while the potential energy savings cannot be determined without defining the SEC norms.

All energy-consuming facilities, even those of the same type, have their own individual characteristics: different operating time from the start of operation, different approach to maintenance, use of raw materials with different quality and chemical composition, location in different climatic conditions, etc. As a result, the norms for specific consumption of FER will also be different. The enterprises may apply old sectoral energy consumption norms or factory standards for specific processing units stated in the process regulations. But most often they do not correspond to the actual condition of the equipment and are applicable to only a few regimes of operation.

The task of defining the SEC norms is further complicated by the quantity and range of the manufactured products. The only approach that is applicable to such systems is to consider the manufactured products and only then to construct a model that takes into account the range and quantity of the manufactured products and all production regimes, even if they change several times during a shift. The specialists from the enterprises admit that they find this task virtually impossible, since the restructuring and adjustment of the equipment for manufacturing a particular product involves the consumption of energy resources and time and effort of the personnel. As a result, many factories are using inaccurate norms per unit.

Not a single enterprise in all the industries among those where our experts have worked has functional models that take into account all the key factors affecting the standard consumption of FER. Some people take into account the climatology by creating a single-factor model. i.e. operation in the winter-time and in the summer-time regimes. This fails to take into account the operating regimes and individual characteristics of the products manufactured in different regimes.  Long ago we understood the need to create a model for calculating the norms for specific consumption of FER which would take into account the impact of all the factors that are significant for the expenditure of energy resources, that is, a model that would be dynamic over time.

We were first assigned such a task by petrochemical holding Sibur. We needed to create a software product based on the physical models of all the main energy-consuming equipment. We understood the task clearly, but completing it at that time seemed impossible, as constructing all required physical models would have taken years of work.

Similar problems emerged later in several other oil refining companies. Faced with the requirement to build the physical models of processing units, we realized that this task was formulated incorrectly. We still had to introduce additional coefficients into the developed models and use the statistical data for the each unit. It became evident that when a processing unit was described by a physical model the actual physical condition of that unit was overlooked.

For uncomplicated studies it is possible to create physical models of simple technical devices. However, this is normally done for academic purposes. What is applicable for a simple device will not be suitable for a complex processing unit. We are familiar with such work on gas compressor units and natural gas air coolers. The physical models built for these devices provide the basis for configuring the most effective operating regimes in different climatic and other conditions. But building such models is possible only for certain types of the equipment and this is intended primarily for the manufacturer, who obtains valuable information for developing the next generation of these devices.

Currently our researchers have arrived at the following. Models are created that take into account the complete history of the life cycle of the equipment or the production process. They consider previous information to determine various parameters of energy efficiency and norms. Over several years, the enterprise accumulates an extensive history of operating units. Therefore, it can be observed how the performance parameters change which regimes ensure the highest efficiency of the units and which do not and what this depends on. Actually, the enterprises are already trying to do this type of work to the extent of their capabilities by using the software that they already have. For instance, for three years in a row the standard specific consumption was X, and this year it became Y. The reasons could be any changes in operating regimes, the amount of processed or transported raw materials, an increase in physical wear of the unit, etc. All of this can have an impact individually and in combination. Thus we are faced with the problem of determining which factors have in fact contributed to the increase in the energy consumption, whether they were objective or not, whether it is possible to influence them in some way, to correct, to take action in order to reduce the impact of these factors. The problem was formulated this way a relatively long time ago. For several years the experts in our Research Department were working towards the solution. With some effort it has now been solved.

For operating a software product that calculates the norms for specific consumption of FER, we need the historical data on the FER consumption, on manufactured products and parameters of operating regimes. Based on our experience, not all manufacturers have sufficient and complete statistics on the various factors that affect the FER consumption. However, many large enterprises have been serious about accumulating the statistical data and have been doing this for a long time. We are aware of cases when the companies introduced information systems in the early 1990s which recorded the consumption of different types of energy resources and the productive operation, for example, the volume of oil extraction or gas transportation, operating process parameters and climatological factors.

With the sufficient source statistical data, the program should be able to automatically determine the key parameters that affect the norms for specific consumption of FER, such as the quantity and quality of manufactured products, the volume and fractional composition of raw materials, operating regime, in other words, its specific characteristics and external factors - any external impact ranging from climatology to directives by the management regarding the quantity of product output.

The program must also bring these rates up-to-date automatically and with a defined frequency. This may be required both in connection with an increase in the number of observed technological and climatic parameters - when installing new sensors, expanding the metering systems, and in connection with changes in the technical condition of the main equipment, such as normal wear and tear or upgrade.

In addition, a user-friendly interface is desirable as those who work under the supervision of the chief power engineer are not programmers. When developing our software product, these considerations led us to provide prompt notification in the event of exceeding the rates or achieving savings, a kind of an online response tool for all unusual incidents. It should be understood that when discussing the FER consumption rate, we do not mean a single number. As an example, the graph (Figure 1) shows a range of rates, or, more precisely, the expected values of one of the energy efficiency indices for a single resource.

How may the calculation of FER consumption rates improve in the future? We believe that the creation of self-learning software is a serious step forward. From that moment on, their further development depends on the enterprises that adopt the product. This dependence is direct – advancement in metering of all types of FER in  manufacturing will improve the capabilities and toolsets provided by the software.

Studies of modern global trends shows that today the following products generate some interest due to the degree to which they meet the stated requirements: cpmPlus Energy Manager by ABB, StruxureWare by Schneider Electric, EMC by Siemens, SRP by Verisae and Energy Dashboard by Honeywell. An analysis of solutions from these and more than two hundred other companies can be found in analytical studies by such independent companies as Verdantix (Green Quadrant Energy Management Software 2013) and Groom Energy Solutions (Enterprise Smart Grid Vendors).

Despite the number of proposed solutions and the availability of solutions from global brands, it can be noted that they are all focused on collecting and visualizing the actual values of the specific consumption and operating parameters, while only a few of them (ABB, Schneider Electric) attempt to create simple models of the norms for the  specific consumption of FER. The software products used by Russian enterprises (Tobolsk-Neftekhim, Gazprom Neft - Moscow Oil Refinery, Gazprom Transgaz Yugorsk, Gazprom Dobycha Nadym) possess not only user-friendly visualization tools, but can also construct multi-factor statistical and physical models for the usage on specific types of the equipment.

The experience of using the software products that allow calculating the norms for consumption of FER shows that their payback period ranges from six months to several years.

Figure 1. The specific consumption of electricity for cooling a gas volume, kWh/Gcal


Source: Smartgrid.ru

Vasily Tarasovsky, Director for Energy Efficiency, GCE Group

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