Energy-efficient operation of pump drives in a cement plant

Abstract

Electric motors consume a large share of electricity in cement industries. Traditionally, most of the motor applications use variable frequency drive to save electricity, but they do not optimally minimize power consumption always. Pumps and fans are the applications where significant energy savings can be obtained at partial load by implementing optimal flux control. The present work identifies 10 large-size pump motors of an integrated cement manufacturing unit and proposes optimal flux control in a novel way during their operation. The proposed method eliminates run-time optimal flux computations, perturbations, and convergence issues as compared to conventional techniques along with excellent dynamic response. Significant savings of $0.237 million in annual energy cost, 3261.6 tons of combusted coal, and reduction of 3359.5 tons green-house gas emissions in a year are estimated at an average 90% loading condition. The estimated energy saving will be in line with “good practice” benchmarks for industries.

Keywords

Energy-efficiency cement manufacturing pump partial load optimal flux operation optimization

Introduction

Presently, the energy needs of the world are mainly fulfilled by burning fossil fuels,^a i.e. oil, gas, coal, and so on. This results in huge amounts of green-house gas (GHG) emissions¹ that contain carbon dioxide (CO₂) as their major component along with other constituents like carbon monoxide, unburned hydrocarbons, particulate matter, sulfur dioxide, and nitrogen oxides. As per reports, a total of 29 Gt of CO₂ emissions occur every year and only quarter of it gets absorbed naturally.² The rest enters into the atmosphere, causing global warming, environmental pollution, and acute and chronic effects on human health.³ Further, due to rapid industrialization and changing lifestyle of human beings, energy demand is expected to increase from 12.5 gigatonne of oil equivalent (GTOE) today to 25 GTOE by the year, 2035 with the simultaneous increase in CO₂ emissions to 75 Gt annually.² The growing consumption of fossil fuels and their sinking reserves^b are enforcing the scientists, researchers, academicians, and the government authorities to work towards implementation of energy-efficient policies and practices by keeping “triple bottom line,” i.e. social, economic, and environmental aspects of a business in view.⁴

Heavy industries such as iron and steel, aluminium, cement, petrochemicals, and pulp and paper are the energy intensive industries. They account for almost 50% of the total industrial energy consumption and around three-quarters of industrial GHG emissions.⁵ These industries are good candidates for implementation of energy-efficient policies and practices. In terms of absolute energy consumption, the cement industry occupies a front position⁶ and consumes approximately 15% of total industrial energy use.⁷ This industry accounts for nearly 10% of the total industrial fuel use⁸ and nearly 8% of global CO₂ emissions.^9,10 The industry is considered to have the largest energy saving potential around 28%–33%,⁹ and this is the motivational factor to choose cement industry in present work for inspecting energy-efficiency opportunities.

Cement manufacturing involves many sequenced processes like raw material extraction, limestone crushing, raw meal grinding, blending, pyroprocessing, cement grinding, packaging, and dispatch.¹¹ The process flow diagram is shown in Figure 1. Today, the best performing plants with pre-heater calciner kilns consume around, 2950 MJ/t (690 kcal/kg clinker),¹² while in some countries, the consumption exceeds even 5 GJ/ton.¹³ The typical electrical energy consumption of a modern cement plant is about 110–120 kWh per tonne of cement.

Figure 1.

Process flow diagram for the cement manufacturing showing energy and heat consumption as well as gaseous and particulate emission.¹¹

The trends of Indian cement industry, the second largest after China, and the world best achieved levels in terms of energy consumption are shown in Table 1 which reveals the huge potential of energy-efficiency improvements in Indian cement industries.⁶

Table 1.

Comparison between Indian trends and world’s best achieved energy consumption level.⁶

Process	Energy consumption	India average	World best practice
Raw materials preparation
Coal mill	kWh/t clinker	8	2.4
Crushing	kWh/t clinker	2	1
Raw mill	kWh/t clinker	28	27
Clinker production
Kiln and cooler	Kcal/kg of clinker	770	680
Kiln and cooler	kWh/t clinker	28	22
Finish grinding
Cement mill	kWh/t cement	30	25
Miscellaneous
Mining and transportation	kWh/t clinker	1.6	1.5
Packing house	kWh/t cement	1.9	1.5
Others	kWh/t cement	2	1.5
Total electric	kWh/t cement	95	77

Chhattisgarh is a mineral rich state which produces nearly 20% of India’s total installed capacity and is emerging as cement hub in central India. With the expansion plan and new green field projects, ACC Limited has set up a new clinker unit in Jamul Cement works with a capacity of 2.79 million tons per annum and allied grinding units in Durg district of Chhattisgarh.^14,15 The plant consumes nearly 60 MW electricity per hour and has been considered for study of energy-efficiency in the present work.

Motor operated systems have significant share in this electricity consumption.^16–18 There may be around 500–700 numbers of motor applications in a typical single kiln cement plant,⁸ mostly operated with variable frequency drives (VFDs). Among them, high-pressure (HP) process fans, blowers, compressors, and pumps are used practically in all stages, and they share major part of total electrical energy consumption in plant.^19–21

Compressed air, pumping, and fan systems involve largely untapped cost-effective areas for improving the energy efficiency. They have unique load characteristics and experience frequent partial loading as compared to any other motor application. Partial loading and idle running are the common undesirable conditions which generally occur with motors driving such loads. Rather, oversizing is quite common and intentionally done in fan and pump applications to account for future increase of gas or liquid flows, and these results in considerable amount of wastage of electrical energy.^17,22–24

The present work is focused on such applications and proposes a novel method of optimal flux operation during partial load conditions in order to reduce electrical energy consumption. The good features of conventional flux optimization techniques are implemented together to run the motor drives at optimal flux level during its variable load cycle. The proposed method totally eliminates run-time computations, optimal flux perturbations, and convergence issues as compared to conventional techniques and earlier works and can be treated as advantages of the proposed method. Significant energy savings have also been obtained as compared to conventional constant flux operations.

General energy-efficiency trends in cement manufacturing

There are many processes, machines, and equipments involved in cement manufacturing which consume very high amount of energy. The energy-efficiency practices should be multidirectional and multiobjective and should be implemented at various levels. The recommendations made by the US environmental protection agency and similar other agencies play a key role in this effort. Efficient control of crosscutting equipment, proper and efficient operation of the process, and ensuring the most efficient technology in place are essential to realize energy savings in a plant’s operation. Time to time replacement of technologies/machines/components, improved control and process automation techniques, process tuning, improved raw material preparation, optimization of fuel mix, optimization of power consumption, waste heat recovery, condition monitoring, regular energy audit, proper load management and load shedding are the measures generally taken for improving energy efficiency.^4,12 A concentrated energy audit was conducted in a cement manufacturing plant located in California in 2007 by a team of experts. Eleven separate energy-efficiency project ideas with energy saving estimation of 12,000 MWh/year were identified in the plant. The operational performance of various equipments/systems such as vertical finish mill fan, compressed air system, tipping kiln induced draft (ID) fan, raw mill recirculation, finish ball mill ID fan, lighting apparatus, clinker cooler fan, and pre-heater section were thoroughly investigated phase-wise. After implementing various energy-efficiency improvement projects, total incentives worth $710,125 were earned cumulatively in three years.¹⁶

In recent years, the industry has addressed and adopted many energy-efficient technologies and alternative arrangements in their manufacturing facilities. There have been many such improvements discussed in various literatures which can be referred by readers.^4,6,25,26 In electrical systems solely, poor power factor, power quality, voltage unbalance, oversized motors, inefficient motors, and improper lighting arrangements have been noticed responsible for degraded electrical energy efficiency. All these issues are considered and corrected in modern cement manufacturing facilities by installing harmonic filters, power factor correction equipments, voltage controllers, strategic motor sizing, and selection as per National Electrical Manufacturers Association guidelines and VFDs. Various-related case studies and its results have been discussed in different literatures.^4,27 Installation of VFDs in electric motor–driven systems like crushers, grinders, mixers, fans, blowers, pumps, and compressors are common practices to improve electrical energy efficiency. Such installations of VFDs have resulted in tremendous energy savings from 10% to 60% in the past along with other benefits.^4,9

Conventional VFDs do not optimally minimize motor input power at any given motor speed and load torque especially in wider speed range applications. Minimization of losses has a great significance in partial load conditions.^28,29 A low-partial load efficiency of motors in some specific applications like pumping, compressors, and fans have been noticed by many authors. They operate over wide speed range and generally are oversized also. These are low dynamic drives operating in the constant torque mode with frequent partial load intervals. Fast dynamic response is not a critical issue in these applications, and most of the time they run far below the rated load. Significant improvements have been achieved by implementing optimal flux control over rated flux control in such conditions especially in steady state.^28,30–35 The present work identifies such applications in various sections of cement manufacturing plant under consideration and proposes a novel way of optimal flux operation of motors utilized there to achieve better energy savings in steady state as well as other performances.

Selected energy-efficiency load points

There are multiple numbers of pump applications of various capacities in the entire process. Out of them, 10 large pumps, driven by electrical motors (all above 50 kW capacities), are selected for detailed investigation of electrical energy efficiency. The list of these motors along with their capacity is shown in Table 2.

Table 2.

Selected pumps from various sections.

Utility	Capacity	Numbers
Water transfer pump for process	55 kW	2
Pump for cooling water recirculation	160 kW	4
Booster pump for fire fighting at Pump House building	55 kW	1
Pump motor for Start Air Compressor Hydraulic drive	160 kW	1
Pump motor for tiling Hydraulic drive	110 kW	1
Pump motor for clamping	75 kW	1

Proposed methodology

It has been observed that motors have very high efficiency near rated load conditions. During partial loading, if motors are operated at rated flux, they would operate below their rated efficiency even at full speed due to over-excitation and poor power factor. Under such conditions, operation of motors at optimal flux level is better for energy saving. In order to reduce losses, motor magnetizing flux is varied, and this is performed by two methods, namely, loss model control (LMC) and search control (SC). LMC is a feed-forward and analytical method which involves loss computation using motor loss model, and then optimal flux value is estimated by implementing optimization principles. Minimization of total loss value is the desired objective during optimization process. This method is quick but suffers parameter sensitivity issues under running conditions. Precise information of motor loss equation parameters is difficult to assess due to skin, temperature rise, and saturation effects, and so these are treated as constraints/drawbacks of this method. In SC method, which works on run-time optimal flux search principle and is of feedback in nature, flux is decremented in steps till the measured DC link input power comes to lowest possible value for delivering the same performance. This method is fully insensitive to parameter variation but has few drawbacks, as it produces objectionable torque ripples, suffers slow convergence problem, and never achieves the optimal operating point. The inner part of the control algorithm may be realized in scalar or in vector control environment. The scalar control method is based on the steady-state model of the motor and can exhibit poor dynamic response whereas flux vector control scheme enables decoupled control of the torque and flux, so it provides better performances.^{22,28–34,36–45}

In the proposed work, good features of LMC and SC are used in a novel way, and remarkable electrical energy savings and dynamic performances are achieved. Firstly, an offline estimation of optimal flux value is done using LMC technique, and then those optimal flux values are directly used to operate the motor in run-time load condition with the help of optimal flux controller as shown in Figure 4. This offline optimal flux estimation process eliminates the need of run-time complex computation, as it usually required in LMC method. It also eliminates the searching process of SC method. Determination of optimal flux value as per projected load condition and development of controller along with performance evaluation is discussed in “Estimation of optimal flux value” and “Design of optimal flux controller” sections. The entire work has been done on MATLAB platform, and the inbuilt simulation model “Vector Control of AC Motor Drive” from “SimPowerSystems Library” is used for performing simulation studies.

Estimation of optimal flux value

In vector control principle, flux is represented by flux component of current, i.e. I_ds. The method used for estimation of motor optimal I_ds has been well established and proved in many earlier research works.²⁹ ^,37 These analytical methods are still advantageous and widely used.^34,35,40,45 By using the induction motor loss model in the field reference frame (Figure 2), the total loss expression is derived first. Later, by applying classical optimization mathematics on this loss expression, the expression of optimal flux component current is derived (equations (1) and (2)). These expressions are function of motor parameters (R_q, R_d, R_s, R_qls, R_r, and M_d) and load parameters in terms of torque (I_qs) and speed (ω). Then, the optimal values of flux component of the current (I_ds*) are estimated offline by using equations (1) and (2), motor parameter values and load parameters. These optimal values ensure minimum loss condition in a motor for any projected load under steady state. The motor parameters are assumed constant which affect the estimations slightly. Also, the inverter losses are not included in total loss expression while deriving optimal flux equations.

i_{d s} = \sqrt{\frac{R_{q}}{R_{d} (ω)}} i_{q s} = K_{m i n} (ω) | i_{q s} |

(1)

K_{m i n} (ω) ≜ \sqrt{\frac{R_{s} (R_{q l s} + R_{r}) + R_{q l s} R_{r}}{R_{s} (R_{q l s} + R_{r}) + M_{d}^{2} ω^{2}}}

(2)

Figure 2.

Steady-state simplified equivalent circuit in field reference frame.

Figure 3.

Conventional vector controller for 215 HP, 415 V induction motor drive.(Source: https://in.mathworks.com/help/physmod/sps/powersys/ug/building-your-own-drive.html)

As sample, the optimal flux values for a 215 HP pump motor are estimated by the above discussed procedure and are shown in Table 3. The table is defined for different load torque conditions (from 90%, 80%, 70%, and 60% of rated load) and different speed variations (100%, 80%, 60%, 40%, and 20% of rated speed), as usually happens in real-time operation. In the same way, the optimal values of I_ds* have been produced for all the other selected pump motors as mentioned in Table 1 for their variable load pattern. Such tables are further used for designing proposed optimal flux controller discussed in Design of optimal flux controller section.

Table 3.

Sample I_ds* values for a 215 HP motor.

Load condition	Torque value (Nm)	I_ds* at rated speed	I_ds* at 80% of rated speed	I_ds* at 60% of rated speed	I_ds* at 40% of rated speed	I_ds* at 20% of rated speed
Full load	1067	223.5	223.3	222.83	222.19	222.14
90% of full load	960	201.55	201.31	200.79	200.27	199.69
80% of full load	854	180	179.51	179.16	178.06	175.85
70% of full load	747	158.05	157.58	157.00	156.48	155.20
60% of full load	640	136.1	135.60	135.14	134.14	132.878

Design of optimal flux controller

All the values of I_ds* generated by using equations (1) and (2), as discussed in the previous section and shown in Table 3, are used for the development of look-up tables in MATLAB. The look-up tables reproduce I_ds* as command signals in the flux vector control loop (shown in Figure 3) instead of its default constant value. The look-up table works as optimal flux controller according to load condition for the selected motor drive as shown in Figure 4. Each aforesaid motor as mentioned in Table 2 is treated individually, and their individual optimal flux controllers are designed in the similar fashion.

Figure 4.

Sample optimal flux controller (look-up table) for 215 HP, 415 V induction motor drive.

Results and discussion

Dynamic and steady-state performance validation

The sample simulation model, shown in Figure 5, is developed to evaluate the dynamic- and steady-state performances of 215 HP, 415 V, 50 Hz pump motor operated at two different operating flux levels, i.e. rated and optimal, at same projected load conditions. Similar models are developed for all the pump motors mentioned in Table 1 individually. Excellent dynamic performance, torque tracking, and speed tracking are observed everywhere while operating at an optimal flux level and found very similar to those obtained with rated flux operation.^c Along with excellent dynamic performances, significant efficiency improvements ranging from 1% to 15% at different load conditions have also been achieved while operating at optimal flux levels under steady state (Figure 6). Hence, considerable amount of electricity savings, as shown in Table 4, is offered by optimal flux control of pump motors at partial loads without compromising with the dynamic performance. The reduction in electrical energy consumption results not only in cost saving but also in reduction of coal consumption and CO₂ emissions, as seen in Tables 5 and 6.

Figure 5.

Sample model of a 215 HP, 415 V, 50 Hz motor for performance validation.

Figure 6.

Speed, torque tracking, Stator Current and Efficiency Performance by rated flux operation (blue) and optimal flux operation (red) at 90% load for a sample 215 HP, 415V, 50 Hz pump motor, OPT: Optimal, VEC: Vector.

Table 4.

Efficiency performance at variable load cycle (90% and 70% of rated load torque) by optimal flux operation (red) and rated flux operation (blue).

Speed	30 rad/s		60 rad/s		90 rad/s		120 rad/s		150 rad/s
Speed	Rated	Optimal	Rated	Optimal	Rated	Optimal	Rated	Optimal	Rated	Optimal
Flux programming
Efficiency at 90% of rated load	15.00%	17.14%	27.27%	33.33%	38.00%	47.30%	49.05%	60.77%	60.90%	70.92%
Efficiency at 70% of rated load Nm	13.63%	14.06%	26.08%	27.27%	36.57%	38.29%	46.16%	51.60%	59.81%	62.47%

Table 5.

Sample savings at 90% of full load at 150 rad/s speed.

Capacity (HP)	Power saving (kW)	Annual electricity saving (8500 h) in kWh	Annual energy cost saving in $
215	48.87	415,395	33,231.6
150	36.65	311,525	24,922.0
100	21.38	181,730	14,538.4
75	15.26	129,777	10,382.1

Table 6.

Total savings in energy, energy cost, coal consumption, and green-house gas emission.

Capacity (HP)	Number of operating units	Annual electricity saving (8500 h) per motor in million units	Total annual electricity saving (8500 h) in million units	Annual energy cxost saving in ($)	Annual coal saving in ton	Annual reduced CO₂ emission in ton
215	5	0.415	2.07	166,158.0	2289.5	2358.18
150	1	0.311	0.311	24,922.0	344	354.32
100	1	0.181	0.181	14,538.4	199	204.97
75	3	0.129	0.389	31,146.3	429.1	442.04
Total			2.951	236,764.7	3261.6	3359.5

From Figure 6, it is clearly observed that the torque and speed performances are almost overlapping with each other, and efficiency performance is improved under optimal flux operation as compared to rated flux operation in partial load condition at steady state. The performances at variable load cycle are also investigated, and it is observed that similar improved results are obtained. A load cycle applied on the selected 215 HP pump motor running at 150 rad/s is defined as, 90% load from 0 s to 4 s, 70% load from 4 s to 6 s, 90% load from 6 s to 8 s, and 70% load from 8 s to 10 s, all at similar speed of 150 rad/s. Similar improved efficiency performance with same accuracy of torque and speed tracking are obtained for the projected load cycle as shown in Figure 7.

Figure 7.

Efficiency performance of 215 HP, 415 V, 50 Hz pump motor at a load cycle of 10 s.

Energy cost saving calculations

The selected pump motors, as detailed in Table 1, were modelled and simulated in MATLAB for performance analysis as explained in “Proposed methodology” section. The current drawn from supply by all of them was measured for both flux control modes individually to deliver same output power at all possible partial load conditions. Further, the estimations of power savings, annual energy savings, and annual energy cost savings are done. Table 5 shows these savings of individual pump motors for one year. Energy costs saving calculations have been done by taking electricity cost of $0.08/kWh.⁴⁶ The average operating hours of motors were taken to be 8500 h in one year.¹⁴ It was assumed that for the remaining 200 h, the motors are under breakdown or scheduled maintenance. Also the load factor of motors was assumed to be 0.9 throughout the operating hours for simplicity. Practically, the load factor is variable and depends on liquid and gas flow heads and patterns. The results shown in Table 5 are estimated for single unit of pump motors. Total energy saving (in kWh), energy cost saving, reduced coal consumption, and reduced CO₂ emissions are calculated further considering all the 10 numbers of motor pumps, and these results are shown in Table 6. A total savings of 2.951 million units in electricity, $236,764.7 in energy cost, and 3261.6 tons in coal consumption were obtained along with reduction in CO₂ emissions by 3359.5 tons in one year. Coal savings are estimated by considering that its 1 kg quantity is burnt to produce 1 kWh electricity.⁴⁷ The reductions in GHG emissions have been obtained by taking the emission factor as 1030 g CO₂/kWh.⁴⁸ Similar improvements are also seen on other load factors for all the pump motors.

Conclusion

The present work focused on reducing electricity consumption of pump motors from different subsections of cement plant located at Durg district of Chhattisgarh, India. A total 10 numbers of large-size pump motors ranging from 75 HP to 215 HP were selected. The selected pump motors were operated at optimal flux level by the proposed novel method in place of rated flux level in partial load conditions. Multidirectional improvements were achieved while implementing optimal flux operation by the proposed method. Firstly, significant energy savings ranging from 1% to 15% are achieved by operating motor at optimal flux level at partial load conditions as compared to rated flux operation. Secondly, the way of implementation of optimal flux values in real-time system is unique, as the optimal flux values are predefined. The proposed method eliminates the need of run-time computation complexity in traditional LMC, hence cost-effective. No run-time perturbations happening, as it usually happen in conventional SC, so no torque ripples, hence less wear and tear of motor drive. This will result in lesser maintenance cost of the system. Thirdly, the working with vector control principle itself insures high-level dynamic performance. The proposed method ensures good dynamic performance along with energy saving both. Generally, dynamic performance gets degraded while looking for efficiency improvement. Also, the performances obtained in load transitions are seen excellent. Above all, a total savings of 2.951 million units of electrical energy and $0.237 million in energy cost have been observed in one year. On environmental front, it is found that there will be a reduction of 3261.6 tons of coal usage and around 3359.5 tons of CO₂ emissions into the atmosphere annually. Thus, it will be a remarkable contribution towards social health and environmental protection.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Prashant Choudhary graduated in Electrical and Electronics Engineering from Birla Institute of Technology, Mesra, India in the year 2003. He completed his Master of Technology (Instrumentation and Control) from Bhilai Institute of Technology, Durg, (CSVTU) in 2009. At present, he is working as Associate Professor in the Department of Electrical Engineering at J.D.C.O. E.M, Nagpur. His areas of interest include energy efficiency, optimization, artificial intelligence, smart grids and renewable energy systems.

Satya Prakash Dubey has received his Bachelor degree in Electrical Engineering from Government Engineering College (Now National Institute of Technology) Raipur, India in the year 1995. He completed his Master in Engineering (Power Apparatus and Electrical Drives) from Indian Institute of Technology, Roorkee, India in 2000. He obtained his Ph.D. from Birla Institute of Technology and Science, Pilani, India in 2006. Currently, he is a Professor in the department of Electrical Engineering, R.C.E.T. Bhilai, India. His research areas include power converters, active filtering, power conditioning, AC and DC drives, energy efficiency, renewable energy and its applications, neural network and fuzzy logic based controller design for electrical drives and genetic algorithm technique for LC filter design.

References

Hook

Tang

Depletion of fossil fuels and anthropogenic climate change—a review. Energy Policy 2013; 52: 797–809.

Abas

Kalair

Khan

Review of fossil fuels and future energy technologies. Futures 2015; 69: 31–49.

Asif

Muneer

Energy supply, its demand and security issues for developed and emerging economies. Renew Sustain Energy Rev 2007; 11: 1388–1413.

Worrell

Galitsky

Energy efficiency improvement and cost saving opportunities for cement making. Berkeley, CA: Environmental Energy Technologies Division, Energy Analysis Department, Ernest Orlando Lawrence Berkeley National Laboratory, 2008.

United Nations Industrial Development Organization. Global industrial energy efficiency benchmarking, 2010. http://apki.net/wp-content/uploads/2012/07/Global-Industrial-Energy-Efficiency-Benchmarking-An-Energy-Policy-Tool.pdf.

Radwan

MA.

Different possible ways for saving energy in the cement production. Adv Appl Sci Res 2012; 3: 1162–1174.

Madlool

Saidur

Hossain

et al . A critical review on energy use and savings in the cement industries. Renew Sustain Energy Rev 2011; 15: 2042–2060.

Eskom. Concrete steps towards profitability, 2011. http://www.eskom.co.za/sites/idm/Documents/128251_Cement_Brochure.pdf.

Kajaste

Hurme

Cement industry greenhouse gas emissions—management options and abatement cost. J Clean Prod 2016; 112: 4041–4052.

10.

Choate

WT.

Energy and emission reduction opportunities for the cement industry. Germantown: U.S Department of Energy, 2003.

11.

Huntzinger

Eatmon

DT.

A life-cycle assessment of Portland cement manufacturing: comparing the traditional process with alternative technologies. J Clean Prod 2009; 17: 668–675.

12.

Bhattacharya

Saha

High level automation to achieve improved productivity, energy efficiency and consistent cement quality. In: 2015 IEEE-IAS/PCA cement industry conference, Toronto, Canada, 26–30 April 2015, pp. 1–7. Piscataway, NJ: IEEE.

13.

Engin

Ari

Energy auditing and recovery for dry type cement rotary kiln systems––a case study. Energy Convers Manag 2004; 46: 551–562.

14.

India brand equity fund. http://www.ibef.org (accessed 16 May 2016).

15.

Cement directory, http://cement.industry-focus.net/ (accessed 16 May 2016).

16.

Sperberg

Ruegg

Mining for energy efficiency in cement plants a teamwork approach. In: 2012 IEEE-IAS/PCA 53rd cement industry technical conference, San Antonio, TX, 14–17 May 2012, pp. 1–5. Piscataway, NJ: IEEE.

17.

Abrahamsen

Blaabjerg

Pedersen

JK.

Energy efficiency improvements in electronic motors and drives: efficiency improvement of variable speed electrical drives for HVAC applications. Berlin: Springer, 2000, pp. 130–135.

18.

United Nations Industrial Development Organization. Motor systems efficiency supply curves, 2010. https://www.ctc-n.org/sites/www.ctc-n.org/files/resources/unido_-_un-energy_-_2010_-_motor_systems_efficiency_supply_curves_2.pdf

19.

Cement Sustainable Initiative. Existing and potential technologies for carbon emissions reductions in the Indian cement industry, 2013 https://www.ifc.org/wps/wcm/connect/0a7431004fa1997eac97ee0098cb14b9/india-cement-carbon-emissions-reduction.pdf ?MOD=AJPERES

20.

Viholainen

Tamminen

Ahonen

et al . Energy-efficient control strategy for variable speed-driven parallel pumping systems. Energy Effic 2013; 6: 495–509.

21.

Jahmeerbacus

MI.

Flux vector control of an induction motor drive for energy-efficient operation of a centrifugal pump. In: 2015 international conference on industrial engineering and operations management, Dubai, UAE, 3–5 March 2015, pp. 1–6. Piscataway, NJ: IEEE.

22.

Saidur

Mekhilef

Ali

et al . Applications of variable speed drive (VSD) in electrical motors energy savings. Renew Sustain Energy Rev 2012; 16: 543–550.

23.

Mirzaeva

Sazdanoff

The effect of flux optimization on energy efficiency of induction motors in fan and pump applications. In: 2015 Australasian universities power engineering conference, Wollongong, Australia, 27–30 September 2015, pp. 1–6. Piscataway, NJ: IEEE.

24.

Ahonen

Tamminen

Viholainen

et al . Energy efficiency optimizing speed control method for reservoir pumping applications. Energy Effic 2015; 8: 117–128.

25.

Radgen

Blaustein

Compressed air system in the Europian Union: energy, emissions, saving potentials and policy actions. Stuttgart: LOG-X Verlag GmbH, 2001.

26.

Worrell

Martin

Price

Potentials for energy efficiency improvement in the US cement industry. Energy 2000; 25: 1189–1214.

27.

Segal

Energy efficiency improvement in the cement industry. In: 2002 IEEE-IAS/PCA 44th cement industry technical conference, Jacksonville, FL, 5–9 May 2002, pp. 53–62. Piscataway, NJ: IEEE.

28.

Turner

McCormick

Cleland

JG.

Efficiency optimization control of AC induction motors: Initial laboratory results. Washington, DC: United States Environmental Protection Agency, Research and Development, National Risk Management Research Laboratory, 1996.

29.

Chakroborty

Hori

Fast efficiency optimization techniques for the indirect vector-controlled induction motor drives. IEEE Trans Ind Appl 2003; 39: 1070–1076.

30.

Yatim

AHM

Utomo

WM.

To develop an efficient variable speed compressor motor system. Skudai: Universiti Teknologi Malaysia (UTM), 2007.

31.

Yakhelef

Energy efficiency optimization of induction motors. Boumerdes: Boumerdes University, 2007.

32.

Fletier

Eichhammer

Schleich

Energy efficiency in electric motor systems: technical potentials and policy approaches for developing countries. Vienna: United Nations Industrial Development, 2011.

33.

Kumar

Chelliah

Srivastava

SP.

Adaptive control schemes for improving dynamic performance of efficiency-optimized induction motor drives. ISA Trans 2014; 57: 301–310.

34.

Xie

Wang

FOC for loss minimization of induction motor using SVM. Metall Min Ind 2015; 4: 79–84.

35.

Kuriakose

Sukeshkumar

Beevi

MW.

Efficiency optimization of vector controlled induction motor drives. In: 10th National conference on technological trends, Trivandrum, India, 6–7 November 2009.

36.

Garcia

Mendes Luis

Stephan

et al . Fast efficiency maximizer for adjustable speed induction motor drive. In: Proceedings of the 1992 International conference on industrial electronics, control, instrumentation, and automation, San Diego, CA, 13 November 1992, pp. 37–42. Piscataway, NJ: IEEE.

37.

FemBndez-Bernal

Garcia-Cerrada

Faure

Model-based loss minimization for DC and AC vector controlled motors including core saturation. In: Thirty-fourth IAS annual meeting on industry applications, Phoenix, AZ, 3–7 October 1999, pp. 1608–1615. Piscataway, NJ: IEEE.

38.

Vukosavic

Levi

Robust DSP-based efficiency optimization of a variable speed induction motor drive. IEEE Trans Ind Electron 2003; 50: 560–570.

39.

Almeida

DDS

Filho

WCPDA

Sausa

GCD.

Adaptive fuzzy controller for efficiency optimization of induction motors. IEEE Trans Ind Mot 2007; 54: 2157–2164.

40.

Uddin

Nam

SW.

New online loss-minimization-based control. IEEE Trans Power Electron 2008; 23: 926–933.

41.

Raj

Srivastava

Agrawal

Differential evolution based optimal control of induction motor serving to textile industry. IAENGI Int J Comput Sci 2008; 35: 201–208.

42.

Raj

Srivastava

Agrawal

Energy efficient control of three phase induction motor—a review. Int J Comput Electr Eng 2009; 1: 61–70.

43.

Bazzi

Krein

PT.

Review of methods for real-time loss minimization in induction machines. IEEE Trans Ind Appl 2010; 46: 2319–2328.

44.

Razali

Abdalla

Ghoni

et al . Improving squirrel cage induction motor efficiency: technical review. Int J Phys Sci 2012; 7: 1129–1140.

45.

Stumper

Dotlinger

Kennel

Loss minimization of induction machines in dynamic operation. IEEE Trans Energy Convers 2013; 28: 726–735.

46.

Cserc.gov.in. Chhattisgarh State Electricity Regulatory Commission, 2016. http://www.cserc.gov.in/admin/upload_terrif_order/033017_104805.pdf.

47.

Cbalance.in. GHG inventory report for electricity generation and consumption in India, http://cbalance.in/wp-content/uploads/2013/01/cbalance_white-paper_Electricity-emission-factors_28Dec2012_revised_V21.pdf (2013, accessed 26 March 2018).

48.

Mittal

Sharma

Singh

Decadal emission estimates of carbon dioxide, sulphur dioxide, and nitric oxide emissions from coal burning in electric power generation plants in India. Environ Monit Assess 2014; 186: 6857–6866.

49.

Dusi

Schultz

Energy management and efficiency—a systems approach. In: 2012 IEEE-IAS/PCA 53rd cement industry technical conference, San Antonio, TX, 4–17 May 2012, pp. 1–8. Piscataway, NJ: IEEE.

50.

Mohr

Wang

Ellem

et al . Projection of world fossil fuels by country. Fuel 2015; 141: 120–135.