Total Equipment Energy Effectiveness (TEEE): A comprehensive model to manage energy in manufacturing

Energy eciency brings considerable benets to society and industry through reducing carbon footprint, helping to protect environment and improving energy security and sustainability. It saves money on fuel bills and boosts growth and creates jobs in the economy. The industry sector is a major contributor to energy consumption and related greenhouse gas (GHG) emissions. It has a lot of potential to further reduce energy use and greenhouse gas emissions. Total Equipment Energy Effectiveness (TEEE) addresses the current challenge of a distinct lack of a comprehensive model to embrace all potential aspects of equipment, manufacturing processes and energy features for measuring equipment energy eciency. The model needs to be exible enough to cater for the needs of every manufacturing rm. It can be used at equipment level, process level as well as the broader level of a factory. The model is a measure of how eciently equipment consumes energy compared to its full potential and can be applied as a tool to improve energy eciency. The comprehensiveness involved might be a potential obstacle to implement a total energy effectiveness method particularly in SMEs. However, the problem is tackled by a novel TEEE Methodology which simplies its application even for small companies. TEEE makes the concept of equipment energy effectiveness clearer and more applicable and also makes communication more ecient and easier within manufacturing industry. It provides a sound perspective on improvement to sustainability and also can be used as a benchmark and tool to promote energy awareness in the factory.


Introduction
Globally the industry sector accounts for more than a third of energy consumption (1) and about 35 percent of energy and process related greenhouse gas (GHG) emissions (2). Almost 80% of these emissions is from energy use and energy e ciency is potentially the most signi cant and economical means for mitigating GHG emissions from industry (3).
The UK Climate Change Act 2008 commits the UK government by law to reducing greenhouse gas emissions by at least 80% by 2050 compared with 1990 levels (4). The UK industrial sector accounts for about 21% of total delivered energy and 29% of CO2 emissions. Although major improvements have been in the energy intensity of manufacturing (de ned as energy use per unit of economic output), signi cant reductions in GHG emissions are still needed (5). A report in 2018 summarises lessons from the Act, after 10 years, for the UK and other countries, on how climate change legislation can be more effective.
According to the report, the Act should be more policy prescriptive, for example by integrating clearer sector targets. Firmer tests are expected as there is concern that the gap is widening between the emissions targets set in law and the policies established to deliver them (6).
The 2015 edition of Energy Technology Perspectives (ETP 2015) shows the vital role of identifying regulatory strategies and co-operative frameworks to advance innovation in areas like variable renewables and carbon capture. It indicates that efforts to decarbonise the global energy sector are lagging further behind for that year. ETP 2015 focuses on setting out pathways to a sustainable energy future and incorporating detailed and transparent quantitative modelling analysis. Energy decarbonisation is under way, but needs to be boosted and recent trends rea rm the need to accelerate energy technology innovation, including through policy support and new market frameworks (7).
Current models for measuring equipment energy effectiveness focus on factors originate either from equipment or from manufacturing surroundings. The models that consider both aspects are too complicated and ignore energy aspect i.e. thermodynamic e ciency and/or renewables perspective. The TEEE methodology is a comprehensive model that covers all equipment, manufacturing processes and energy aspects. TEEE uses a novel structured data framework to facilitate the measure of how e ciently equipment consumes energy compared to its full potential.
According to ETP 2015, in the medium term, the most effective measures for reducing industrial emissions include implementing best available technologies and energy e ciency measures, switching to low-carbon fuel mixes, and recycling materials. Deploying innovative, sustainable processes will be crucial in the long run (7), and The TEEE methodology can be applied as a tool to achieve these targets.

Background
There are three sources for industry greenhouse gas emissions: 1-Greenhouse gas emissions from industry primarily come from using fossil fuels for energy. The industry may outsource energy generation to suppliers that are burning fossil fuels -mostly coal and natural gas.
2-Greenhouse gas emissions from manufacturers such as cement and lime sectors that use alternative non-fossil fuels originated from a wide range of sources, including tyres, plastics, paper and dried sewage sludge, etc.
3-Greenhouse gas emissions from non-energy uses of fossil fuels in certain chemical reactions necessary to manufacture products from raw materials in steel making, chemical processing, etc. (8).
The proposed model covers the use of all types of fossil and non-fossil fuels as a source of energy i.e. sources 1 and 2. Over recent years the share of electricity generation from the renewables has increased. For example this amount in Scotland has increased from 11.7% in 2004 to 42.3% in 2015 (9). Both energy e ciency and renewable energy can contribute to much lower CO2 emissions and signi cant employment opportunities. A clean energy industry can improve energy security, environmental protection and economic bene ts. Renewables and energy e ciency create more jobs per unit energy than fossil fuel technologies and can be applied as an engine for economic growth (10).
International Energy Agency (IEA) estimates that the energy intensity of most industrial processes is at least 50% higher than the theoretical minimum determined by the laws of Thermodynamics (11). Energy e ciency of many processes is very low, and their average energy consumption is extensively higher than the capability of the best available technology (12). The energy intensity of manufacturing in the UK is greater than that of the economy as a whole: while gross value added in manufacturing was 11.2% of GDP in 2010, it accounted for 16.5% of nal energy demand (13).
There are more than 23 million SMEs in the EU which are employing more than 100 million employees and generate 60% of European GDP. They account for a key share of energy consumption and 60-70% of the environmental impact. Financial restrictions such as low capital availability and non-nancial barriers such as inadequate in-house skills and information issues effectively limit the application of energy e ciency measures within SMEs (14). Total production costs can be improved by 10-20% via optimal energy management in the manufacturing industry (18). Many industrial companies still lack proper techniques to effectively deal with energy ine ciency (19). Boosting energy e ciency is one of the top priorities of the EU energy strategy, and manufacturing is one of the sectors with signi cant potential to improve energy e ciency (20).
There is a high number of variables that affect energy consumption of equipment. These variables may originate from equipment conditions or manufacturing surroundings. A methodology based on the equipment aspect can be developed from energy losses within loading time. This approach identi es energy losses during breakdown, setup & adjustment, speed and so on. However, there are other hidden energy losses before loading time which are crucial to measure to determine equipment energy effectiveness. This aspect should also cover energy losses before loading during preventive maintenance, engineering, improvement and non-scheduled times. This aspect monitors the actual energy performance of a machine relative to its performance capabilities under optimal equipment conditions.

Teee Methodology
A methodology based on the manufacturing processes aspect can be developed from energy losses during operation time. This approach considers energy losses due to lack of skills, materials, tools and so on. However, there are other hidden energy losses pre-operation which are vital to measure to determine equipment energy effectiveness. The manufacturing processes aspect should also identify pre-operation energy losses during time losses due to management, organisation, personnel, and inputs and so on. This aspect monitors the actual energy performance of a machine relative to its equipment settings under optimal manufacturing processes.
As previously mentioned, there is also an essential need to develop a new broad model to cover the energy aspect of equipment energy effectiveness. This approach considers thermodynamic e ciency of the process to minimise energy losses due to thermodynamic ine ciencies. If there are technical constrains to identify or address these ine ciencies, Best Practice Energy Per Unit (BEPU) can alternatively be applied.
Combustible fuels accounted for 67.3% (of which: 65.1% were fossil fuels) of total world gross electricity production in 2016 (21). The energy aspect should also cover the types of energy i.e. renewable or nonrenewable to tackle the major problem of reducing GHG emissions. This aspect monitors the actual energy performance of a machine under optimal energy usage. As shown in Figure 1, the TEEE model is a comprehensive framework that covers all equipment, manufacturing processes and energy aspects. TEEE is a measure of how e ciently equipment consumes energy compared to its full potential.
The level of comprehensiveness can be a possible serious impediment to apply a total energy effectiveness methodology especially in small and some medium size rms. TEEE, as a novel methodology, is designed to solve the problem. The technical data provisions originated from OEE measurement can be used as a solid base to develop an appropriate TEEE measurement system.
Overall equipment effectiveness (OEE), as introduced by Nakajima (1988), is seen to be the fundamental way of measuring equipment e ciency and has been extensively accepted as a major quantitative tool for measuring the productivity of manufacturing systems (22). It is the essential measure of total productive maintenance (TPM) and has become one of the most common used metrics for operations.
The concept of OEE is being used increasingly in industry because it quanti es e ciency into a simple number, while revealing the actual effectiveness of a machine.
A high number of manufacturing companies have already developed the appropriate IT structure to collect and analyse data to measure OEE. The system can be simply adapted to meet the requirements of TEEE measurement. As shown in Figure 2 that structured data framework facilitates measuring TEEE. All tiers of both equipment and manufacturing aspects can be integrated to develop four stages of preoperation, gross operation, net operation and value operation. As mentioned later and shown in Figure 4, all energy factors for gross operation, net operation and value operation can be measured via obtaining results of OEE calculations along with access to the Power consumption during Operation (POPE) quantity.
The energy loss analysis scheme during pre-operation time A set of manufacturing surroundings affect the energy performance of equipment during pre-operation time. Anvari et al (2010) show that there are considerable time losses before loading time (23). As shown in Figure 3, these energy losses can be categorised under eight headings: 1-Energy consumption during Non-Scheduled Time (NST) related to production consists of: all consumed energy during time spent on any disruption to the production schedule, time spent on carrying out current orders, general preparation and basic maintenance such as cleaning and lubrication. It can be calculated as follows: The energy loss analysis scheme during operation time A set of machine conditions along with manufacturing processes affect the energy performance of equipment during operation time. Tajiri and Gotoh (1992) classify major time losses during operations into six groups. Breakdown losses, setup and adjustment losses are downtime losses used to determine a true value for the availability of a machine. The third and fourth losses, including minor stoppage and reduced speed losses, are known as speed losses. They are used as a measure of performance rate of a given machine. Rework and yield losses are defined as quality losses that determine the quality rate for the equipment (24). As shown in Figure 4, the energy losses during operation time can be categorised under three headings:

I-Energy Losses during Gross Operation
Breakdown losses are caused by equipment requiring maintenance. One example is the downtime when labour and spare parts are needed to repair the equipment. Setup and adjustment losses are caused by changes in operating circumstances, for example changes in the beginning of production runs or commencement at each shift. These losses include downtime for setup, start-up, and adjustment. Energy consumption during downtime Losses i.e. breakdown, setup and adjustment losses are used to determine Energy-Availability (EA).

II-Energy Losses during Net Operation
Minor stoppage losses are caused by events such as the machine jamming, halting, and idling. Normally a minor stoppage of more than 10 minutes is considered as a breakdown even if no damage has happened to the equipment. Speed losses are caused by decreased operating speed. These losses are calculated on the basis of the ratio of theoretical to actual operating speed. Energy consumption during speed losses i.e. idling, minor stoppage and reduced speed losses are used to determine Energy-Performance rate (EP).

III-Energy Losses during Valuable Operation
Quality and rework losses are caused by defective products manufactured during normal production. Yield losses are caused by unused or wasted raw materials during the early stages of production from machine start up to stabilisation. Energy consumption during quality losses i.e. start up and production rejects are used to determine Energy-Quality rate (EQ).
The energy loss analysis scheme based on thermodynamic e ciency Thermodynamic methods provide a measure of ine ciencies within a process and accordingly the maximum theoretical improvement potential. Although it is accepted this optimal limit will not be reached in practice, it can still be instructive in showing where differences may arise (5). Thermodynamic analysis can outline the extent of energy ine ciencies within the constraints of the existing process along with potential improvements.
As previously mentioned, the energy intensity of most industrial processes is at least 50% higher than the theoretical minimum determined by the laws of Thermodynamics. Motor systems consume about 65% of electricity in industry. Measures can be aimed at improving the aerodynamics of the motor, its windings and applying higher quality magnetic steel. For example, scrap preheating and oxygen injection in steel industry or better membranes for separation and more selective catalysts for synthesis in chemical industry can decrease energy consumption (3). Energy Consumption due to thermodynamic losses are used to determine Energy-Thermodynamic e ciency (ET).
The energy loss analysis scheme based on non-renewable energy consumption According to ETP 2015, among energy end uses, heating and cooling systems offer signi cant potential for decarbonisation that so far have been mostly untapped. They were responsible for 30% of global carbon dioxide (CO2) emissions in 2012 as 70% of heating and cooling demand were relying on fossil energy sources. Broad application of energy e ciency and switching to low-carbon energy sources can lead the fossil share to below 50% by 2050 with renewables (including renewable electricity) covering more than 40% of heating and cooling needs. Direct and indirect CO2 emissions linked to heating and cooling would fall by more than one-third by 2050 (7).
It is vital to improve energy e ciency; however, there is growing concern about global warming, public health, the exhaustion of fossil fuels and energy price stability. This signi es that the suggested model should involve this element of sustainability. A more effective CO2 strategy should concentrate on shifting to renewables. Other substantial bene ts such as reliability and resilience and Jobs and other economic bene ts can also be derived from renewable energy use. The energy revolution scenario indicates that renewable energy can meet more than 80% of the world's energy demands by 2050 (25).
Some organisations may leave this energy factor and focus on other losses. They can add this factor when they are able to make adequate provisions against non-renewable sources. Non-renewable energy consumption is considered to determine Energy-Renewable rate (ER).

Total Equipment Energy E ciency (TEEE) structure
Based on the TEEE methodology and the above loss analysis schemes, TEEE structure can be de ned.
As shown in Figure 4  TEE= EB × EA × EP × EQ × ET× ER As earlier mentioned, some manufacturing rms may leave ER and focus on other losses. They can add this factor when they are ready. TEEE without considering Renewables can be calculated as given: TEEE= EB × EA × EP × EQ × ET All elements of TEE are summarised in Figure 5.

Case Study
Two large international manufacturers were selected for TEEE application. The rms are PT Kerry Ingredients Indonesia, which is a global food company, and PT Astra Daihatsu Motor, which is the largest car manufacturer and second best-selling car brand behind Toyota, in Indonesia.
A form was developed to gather required data which included 5 parts as follows: Part A-General information Part B-Machine, Plant or Process/Duration/Energy Consumption during the above period Part C-Energy Losses before Loading Part D-Energy Losses after Loading, if the OEE measurement for the above period is not available Part E-Energy Losses after Loading, if the OEE measurement for the above period is available Either Part D or Part E may be skipped based on the availability of data for OEE. A template was also developed and included to make further clari cation.
The results show a good practice for both companies. They also present key opportunities for improvement to meet the new sustainability requirements. The case study still continues and the outcome will be presented when the process is completed.

Conclusions
Based on a new scheme for energy loss analysis involving all equipment, manufacturing processes and energy aspects, a new model to measure and analyse equipment energy effectiveness is developed. This comprehensive model covers all aspects but with a novel structure which simpli es its application.
TEEE monitors all major potential dimensions and measures the equipment energy effectiveness for a full process cycle in order to respond to the new sustainability requirements. Also, it provides a sound perspective on energy effectiveness improvement of plants by taking into consideration all energy losses.