Energy and production analysis of a dairy milk factory: A case of study

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INTRODUCTION
The energy performance of a factory is determined by ensuring the synchronization of factory milk, product production, and energy consumption.Energy analysis, which concerns energy management, is also an effective concept in the food industry.Energy density is an important part of the dairy industry's processes and has an important place in the food industry.Bringing energy under control and making it more efficient are effective in the processing of milk and its products.In this study, the energy performance of a dairy facility was determined using the optimization method, accompanied by sample findings.The focus of this research is to establish an appropriate energy performance efficiency through optimization by demonstrating the mathematical relationship between dairy products and energy consumption based on the dairy facility's results and data.First, energy performance and management were determined by considering milk production and energy consumption values for the dairy plant, accompanied by data from the milk processing plant.
Dairy products, one of Turkey's principal agricultural products, play an important role in the economy.A plant that manufactures dairy products was adopted for this study on energy efficiency analysis and management.This study was supported by the scientific research, statistics, and dairy plant procedures.In the case of dairy product manufacturing in Turkey, the facility model was disclosed by developing a link with production based on energy usage for modeling and simulation.The proportion of dairy products produced as a plant output is mathematically related to energy consumption.Model simulations (calculations of cheese, cream, butter, yoghurt, ayran, and other cheese types with raw milk values) were used to assess the amount of goods and by-products that could be made from the facility's present milk vats (boilers) based on product characteristics and capacity [1][2][3].
Life Cycle Assessment (LCA) has also been used in dairy production by developing a model for the facility based on the association between energy consumption and production for modeling and simulation [1,[4][5][6][7][8].Greenhouse gas emissions have been found to be reduced by energy use, proving the system's efficiency [9].This dairy factory is located in the Cappadocia region and produces dairy products, such as cheese (white, cheddar, curd, and tulum), yogurt, butter, and ayran.The annual production capacity of the dairy products is for white cheese at 1,089 [t/y], for cheddar cheese at 259 [t/y], for curd cheese at 9 [t/y], for tulum cheese at 144 [t/y], for yoghurt at 2,513 [t/y], for butter at 3.27 [t/y], and for ayran at 1,224 [t/y].The daily raw milk processing capacity of this dairy plant varies between 10,000 and 100,000 liters.
The production departments for milk and other products in the factory are presented in Figure 1.Cheese (white, cheddar, curd, and tulum), yoghurt, butter, and ayran were produced in dairy and product manufacturing departments.In the production department of milk and its products, energy is used intensively and humidity is maintained under control.Fluid mechanics control is also provided in piping systems between boats and boilers, where milk and its products are produced.The piping and tub-boiler materials consist of high-quality stainless steel.The Statistical Software Programme (SPSS) was recommended for comparative analyses of the literature for consistency and validity of energy data [10][11][12][13][14].The energy values of the plant were determined using mathematical expressions in this research using SPSS data analysis techniques and data reliability and accuracy testing.
Many previous studies have focused on energy consumption, production charts, and specific energy consumption [15][16][17][18][19].The annual energy efficiency of factories is indicated by an effective analysis of production and energy, as well as a numerical calculation for energy consumption analysis [20][21][22][23][24][25][26][27][28].Furthermore, some energy analysis algorithms have been proposed in the literature and are presented as an analysis of the relationships between energy consumption and facility production [21][22]29].
The study analyzes data accuracy, reliability, and SPPS analysis, highlighting energy consumption and production values and facility efficiency improvements.Energy management and performance analysis also affect the future planning of the facility and encourage an increase in production and energy efficiency.Beginning at the facility's entrance, controlling raw materials and energy is achievable for efficient energy use with an energy management unit and control of all inputs and outputs.
The novelty of this study is that optimizing product efficiency and energy consumption in the production of milk and dairy products positively increases the energy efficiency of factories.Furthermore, optimal conditions for milk and dairy product production can be derived, and energy savings can be proven in milk dairy factories' similar product processes.

MATERIALS AND METHODS
The production processes of dairy factories begin with the purchase of raw milk and continue through separation, pasteurization, cooling, calcium chloride addition, starter culture, pre-maturation, fermentation, clot technique, portioning, brine technique, ripening processes, packaging, storage, and shipment (Figure 2).The capacity of milk and its products varies depending on the product, and is related to energy-consuming machines.In this study, energy-efficient instances are presented by attempting various optimizations through case studies [24,30].
All processes, from raw material input to packaging, storage, and shipment, were evaluated during the dairy factory production process.Thus, calculations are made with the help of thermodynamic laws by developing methods based on the specific energy consumption, energy efficiency, and production amounts of milk and products.
Cheese (white, cheddar, curd, and tulum), yoghurt, butter, and ayran production capacity can be calculated according to the plate pasteurizer capacity using Eq.(1) to Eq. ( 16) [30][31].Some assumptions can be made regarding the production of dairy milk products (working days, milk efficiency of the products, and number of charges).  of milk and product is equivalent to 1000 [kg], and since 1 ton of milk and product is 971 [L], capacity calculations are made by assuming that 1 ton of milk is approximately 1 [m 3 ].The annual value of the plate pasteurizer capacity (PPA) can be determined using Eq.(1) as follows [30]: where PPH is the mass per unit time of the plate pasteurizer at 10 [t/h] (catalogue value of the plate pasteurizer), and η eff is the efficiency factor of the PPA (85%).The white cheese production capacity (CWP) can be obtained using Eq. ( 2) and Eq.(3) as follows [30]: where CW ML is the dairy milk production for white cheese, V CW is the fermentation vessel volume of white cheese (m 3 =t), NC is the number of charges (1.0) for white cheese, n is the number of working days per year (300 days per year), η eff is the efficiency factor of CW ML (80%), and η CW is milk efficiency (16.5% for white cheese).Cheddar cheese capacity production (CCP) can be obtained using Eq.(4) and Eq.(5) [30]: ) where CC ML is the dairy milk production for cheddar cheese, V CC is the vessel volume of cheddar cheese (m 3 =t), NC is the number of charges (2.0) for cheddar cheese, η eff is the efficiency factor of CC ML (80%), and η CC is milk efficiency (9.0% for cheddar cheese).Curd cheese capacity production (CUP) can be obtained from Eq. ( 6) and Eq. ( 7) [30]: ) where CU ML is the dairy milk production for curd cheese, V CU is the vessel volume of curd cheese (m 3 =t), NC is the number of charges (0.5) for curd cheese, η eff is the efficiency factor of CU ML (80%), and η CU is milk efficiency (9.0% for curd cheese).Tulum cheese capacity production (TUP) can be obtained using Eq. ( 8) and Eq. ( 9) [30]: where TU ML is the dairy milk production for tulum cheese, V TU is the vessel volume of tulum cheese (m 3 =t), NC is the number of charges (1.0) of curd cheese, η eff is the efficiency factor of TU ML (80%), and η TU is milk efficiency (10.0% for curd cheese).The Yoghurt capacity production (YOP) can be obtained using Eq.(10) and Eq. ( 11) [30]: x NC [-] x n [d/y] ( 10) where YO ML is the dairy milk production for the yoghurt, V YO is the amount of fermented milk put in each cart (basket) according to the packaging type volume (0.5 m 3 =t), RA is the room amount (2 pieces), FMR is the number of carts (baskets) for each room (6 pieces), NC is the number of charges (amount 2.0) of the yoghurt, ρ YO is the specific weight of the yoghurt (1.042 kg/L), and η YO is the milk efficiency (67% for the yoghurt).Butter capacity production (BTP) can be obtained using Eq. ( 12) and Eq. ( 13) [30]: where BT ML is the dairy milk production for the butter, CR is the volume of crema in the churn volume (60%), V BT is the vessel volume of the butter (0.05 m 3 =t), NC is the number of charges (1.0) of the butter, EF is the efficiency factor (85%), an dη BT is the butter efficiency (42.7%).The Ayran capacity production (AYP) can be obtained using Eqs.( 14), Eq. ( 15) and Eq. ( 16) [30]: where AY ML is the dairy milk production for the ayran, V AY is the vessel volume of the ayran (1.1 m 3 =t), NC is the number of charges (1.0) of the ayran, AY is the vessel volume efficient (70%), ρ AY is the specific weight of the yoghurt (1.042 kg/L), WA is the water addition rate to the ayran (120%), AY FMC is the ayran filling machine capacity (t/y), AYB is the number of bottles/boxes (3000 pieces/h), m AYB is the net ayran mass in a bottle box (0.2 kg), and η AY is the ayran efficiency (85%).
SPSS (version 18.0) was used for the data analysis software was included.Regression analysis was used to examine the data reliability of the scenarios.The R 2 values are presented in graphs.Thus, the fact that R 2 is close to 1 indicates that the results of the study provide reliable data and are expressed mathematically.A p (< 0.05) value was shown to be statistically significant.

Specific Energy Consumption (SECT) and Energy Target Plan (Food Engineering Fundamental Operations)
The specific energy consumption and energy target plan (food engineering core operations) for the SECT (specific energy consumption) table of the dairy processing plant were laid out based on the current situation and case studies.Energy consumption expenditures according to the production of food products were applied according to the following equations [15,24,[32][33][34].
In this generation-energy model, in addition to SECT and ECT (energy consumption), modeling is performed by establishing a mathematical relationship between the heat and electric energy.Owing to the energy consumption (month/year) correlations, functional relationships between the production amount and heat-electrical energy consumption were established and revealed by linear regression analysis in the estimation method.In this estimation method, the R 2 value (determination coefficient) represents the basic annual energy equation [24,32,35].
Under the details of all energy disclosures, the energy consumption for the dairy milk production plant was calculated using Eq. ( 17) [24,32]: (17) where ECT is the energy consumption of the dairy milk plant (GJ), j is the number of equal energy measurements per year up to n j , Q is the volume of production [kg], and i is a symbol.m is the mass of production, which can be expressed as [24,32].
The total estimated project of the specific energy consumption ( ) can be determined using Eq. ( 18) as follows: (18) The total target project of the specific energy consumption ( ) can be determined using Eq. ( 19) as follows: (19)

LCA and Waste Water Management of the Factory
Life-cycle assessment is available for dairy products.The product characteristics of the milk boats currently in use at the milk processing facility were used to calculate the capacity amounts of dairy products.The life cycle assessment in this model was established by estimating the expenses associated with processing and recycling raw milk [1,[4][5][6][7]36].
Because of the basic facility procedures, calculations were performed for the LCA of the milk production facility, as shown in Figure 3.The milk supplied is processed at the plant using power, fuel, packaging and cleaning procedures, water, raw materials, and fuel to create cheese (white, cheddar, curd, and tulum), yoghurt, butter, and ayran, respectively.Water and wastewater are released after production, which highlights their environmental impact.
Figure 3 depicts the LCA of the milk production facility with respect to wastewater consumption.Underground water resources are being treated and a facility certificate for water use is in place.Administrative building wastewater management was also implemented in the facility, and a total of 3600 L/d of wastewater was generated daily with an individual water consumption of 150 L/d of 24 personnel.This wastewater is discharged into the municipal sewer system (Sewer Connection Permit).In industrial wastewater management, whey formed as a result of milk processing in the facility is collected and disposed of in the tank with the help of pumps.

Data and Equipment Analysis of the Production: Uncertainty Analysis
Uncertainty analysis, which shows the precision of measured data, is useful for analyzing production data and tools [7, [36][37][38][39][40][41][42].To enhance the trustworthiness of data analysis, it was necessary to perform an uncertainty analysis of the collected measurements.This highlights the degree of uncertainty in both the systematic and predictive aspects of the data in Eq. ( 20) [7,[39][40][42][43][44][45][46]: (20) where z j is the uncertainty of the dependent variable, z C is the uncertainty of the result, and C is an independent variable.Uncertainty analysis can be used to generate an analytical expression for energy measurement error.The analysis of the uncertainty method describes what is expected from the data of the output or suggests the strategy of the data (the experiment or determination results) from Eq. (21) to Eq. ( 23) [43,45,[47][48] as follows: where is the uncertainty of the result, and C is the independent variable of the result.
In Table 1, a sensor of equipment can be defined as a DC power source with voltage control and a type-T thermocouple; uncertainty in a relative sense is obtained from the accuracy of the data and the value of the measurement.The uncertainty in the relative sense range was estimated to be between 0.01% and 0.4%, and it was revealed that these values were within the uncertainty analysis values.The study of uncertainty was premised on the fact that it is difficult to quantitatively evaluate all the many components involved.An overall dependability of 99.99% was obtained after analyzing the uncertainty of the four primary analytical parameters.

Calculation of the process's thermal and electrical energy
The total energy consumption was calculated from the thermal and electric energy of the machines and equipment in the factory process.It was received from the factory because of the calculations and measurements of the thermal and electrical energy in all machinery and equipment.As a result, the thermal and electrical energies of each process carried out within the factory were analyzed in this study.The thermal and electric energies of the machine and equipment were provided by a homogenizer, cheddar packing machine, coolant tank, cheddar melting-transferring machine, cold storage, separator, tulum cheese filling machine, milk pump, heat exchanger, evaporator, cheddar dry scalding machine, ayran process tank, heating boiler, walled cooking vessel, cooking vessel, compressor, vacuum machine cooling unit, tulum cheese pressing machine, weighing machine, churning machine, cheddar fermentation tank, brine vessel, pressing machine, chopping machine, and ayran filling machine.
The total power ( ) amounts of the processes (electric energy) were calculated according to the following from Eq. ( 24) to Eq. ( 25) as follows [49]: where is total energy of the process, n is denoted of the process number, l n is the consumption of the current, and U n is the consumption of the voltage energy.
The total heat transfer of the processes ( ) was determined by considering the Thermodynamic Tables based on the temperature changes in the process (thermal energy) from Eq. ( 26) to Eq. ( 28) by the 1 st Law of Thermodynamics as follows [49]: DC power source with voltage and current control where is the process of total heat transfer input, is the process of work power, is the mass flow rate, is the specific heat capacity, is an internal energy difference, is a kinetic energy power, is a potential energy power, and ΔT is a difference temperature.However, the internal energy, kinetic energy power, and potential energy power were ignored because of the low energy in the energy calculations.
This system is generally regarded as an open system that features steady flow output.The 1 st Law of Thermodynamics is given by Eqs.(29) to Eq. ( 30) [45][46]: The amount of steam energy generated in all the processes was determined by considering the thermodynamic tables according to the enthalpy changes of the steam at a certain pressure from Eq. ( 31) as follows [49][50]: (31) where is the steam heat transfer of the process, is the mass flow rate of the steam, is the enthalpy of the steam, and is the enthalpy the condense.
The energy efficiency (η I , The 1 st Law of Thermodynamics) can be formulated from Eq. ( 32) and Eq. ( 33) as follows [49][50]: (32) (33) where the thermal (steam) energy, is the energy, and η I is the energy efficiency of the factory.

RESULTS AND DISCUSSIONS
The total amount of production that took place in the dairy and product plant was used as a contributing factor in determining the amount of energy consumed overall.The dairy industry consumes thermal energy [51].Thermodynamic modeling was applied to the thermal performance of a dairy factory in this study [52].Therefore, an investigation of the efficiency of the factory's utilization of energy was carried out using statistics on the production and consumption of energy at the plant.To make the manufacturing facility as effective as possible, an investigation was conducted on the connection between the use of energy and the creation of materials.Table 2 presents the dairy milk facility data for the energy consumption analysis in this study.Pasteurization shows only the raw milk processing capacity and is not included in the production of milk and its products.The pasteurizer capacity was estimated as 20,400 [t/y].In case studies of dairy production, various optimization data were determined according to tank capacity and work time.
Table 2 displays the results of the case studies using the optimization method based on the current yearly capacity.When assessing a dairy milk facility's data profile, the
[ total daily milk yield was derived by subtracting the energy analysis production efficiencies, which were based on the production and efficiency of the various milk products.Annual production efficiency can be calculated using the product per total dairy product [53].The total dairy product was determined as 4,311.99[t/y] with a production efficiency of 21.1% for ACP; 4,940.73[t/y] with a production efficiency of 24.2% for Case 1; 5,575.17[t/y] with a production efficiency of 27.3% for Case 2; and 6,209.60 [t/y] with a production efficiency of 30.4% for Case 3. Table 3 presents the energy consumption of the dairy milk factory using machinery and equipment.The output power capacity was calculated using the dairy milk factory process.In factories, there are many machines and equipment used for the processing of dairy milk products.These conditions were considered in the computation.The total power of the process was calculated at 180. 52 [kW] in the factory.
In Table 3, the energy consumption of the dairy milk factory by the machinery and equipment homogenizer can be obtained as follows: the cheddar packing machine, coolant tank, cheddar melting-transferring machine, cold storage, separator, tulum cheese filling machine, milk pump, heat exchanger, evaporator, cheddar dry scalding machine, ayran process tank, heating boiler, walled cooking cooking vessel, compressor, vacuum machine cooling unit, tulum cheese pressing machine, weighing machine, churning machine, cheddar fermentation tank, brine vessel, pressing machine, chopping machine, and ayran filling machine.
Table 4 presents the dairy products of the existing products based on a worldwide asset evaluation.These dairy products are pasteurizers, total cheese, crema, yoghurt, and butter, which were estimated by averaging the energy machinery and equipment estimates of the enterprise's daily milk and products per final product mass according to Table 2 [5].In Table 4, the total energy consumption (ECT) was found to be 29,303.18[GJ] for the input of the annual current production.The total energy was found to be 31,663.1 [GJ] for the input of annual current production for Case 1.In Case 2, the ECT was found to be 34,019.2[GJ/ kg] for the input of the annual current production.In Case 3, the ECT was found to be 36,375.4[GJ/kg] for the input of annual current production.Table 4 shows the relationships between the optimizations and energy and production based on these values.These optimization values were generated and graphed using 3 case studies.
The total energy and raw milk data in Table 4 were analyzed using the current status of the 3 case study comparisons.Thus, statistical analysis was performed between the energy and production values in this study.
Figure 4 shows the ECT and TDP results for the case study.In the regression analysis, the R 2 was 0.9989 for ECT and 0.9973 for TDP.When the regression curves in Figure 4 were evaluated with logarithmic slopes, it was determined  that the values for Cases 1 and 2 reached the optimum point.Thus, the most efficient and optimal situation for dairy plants was revealed.
In Table 5, calculations were made according to the energy consumption values (heat boiler and electric energy) taken from the dairy and products facility for a period of 1 year.The annual energy consumption (ACP) of the total factory was 29,303,200 [MJ/y].Based on these calculated values, the SECT values of the factory were optimized according to the case studies.The result of the SECT value was found to be Case 1 at 6,408.60 [MJ/t], Case 2 at 6,101.92 [MJ/t], and Case 3 at 5,857.92 [MJ/t].The SECTT value was calculated for Case 1 as 6,352.14[MJ/t], for Case 2 as 5,898.67[MJ/t], and for Case 3 as 5,445.21[MJ/t].Therefore, the case study results (SECT and SECTT) are available according to the study by Nathaphan and Therdyothin [24].The results of the 3 case studies are compatible with the results of the specific energy consumption in Table 5.
The SEC values of the dairy milk plant in each of the three case studies are shown in Figure 5.The overall estimated energy consumption of the dairy plant demonstrates the relationship between the specific energy consumption and the total goal-specific energy consumption.The SECT of ACP was calculated to be Cases 2 and 3 illustrate the energy efficiency of the case study.SPSS software (version 18.0) was also used for data analysis and evaluation.The reliability of the scenario data was investigated using regression analysis.The R 2 value was 0.9609 for ECT, 0.9903 for SECT, and 0.9587 for SECTT, and the equations are depicted in the graph.
Table 6 shows that the factory operates nearly full time for 150 days per year.The steam boiler uses 244.6 [t/y] powdered lignite coal for annual thermal energy.The calorific value of the coal used in the heat duct was  Thus, despite the factory's thermal and electrical energy expenditures, the energy efficiency was calculated to be 45.5%.Although the energy efficiency is calculated to be approximately 45.5%, the main reasons for the waste of the remaining 54.5% are the energy losses in the processes due to the fluid nature of milk, large amount of energy consumed, and high amount of waste heat.It is recommended to further optimize the processes that need to be initiated in the factory and install a recycling waste heat system.Thus, the energy efficiency of a factory can be further increased using these methods.
In Table 6, the electrical energy results in this case study are in agreement with studies in the literature, as are the SECT values.Lincoln et al. [54] investigated a milk evaporator system case study that presents the processes and end creation of a novel, fully electric milk evaporator system design that uses 3593 [kW] of energy.According to this case study, the results of Buhler et al. [55] are similar to those of the energy efficiency of a milk processing factory.Basaran et al. [50] discovered that a unique design for 10 [t/h] milk could be completed with 44.35% less energy  input than a standard application.Considering the total energy efficiency of the dairy milk factory, case studies were optimized, and the energy efficiencies were separately revealed.The energy efficiency of the factory was determined according to 3 different scenarios optimized for thermal and electrical energies according to the actual total energy efficiency.Figure 6 demonstrates the energy efficiency of the dairy milk factory in the three case studies.The input energy was calculated to be 374.24 [kW] in Case 1,356.33 [kW] in Case 2, and 342.08 [kW] in Case 3. The energy efficiency in this case study was assessed to be 48.2% for Case 1, 50.7% for Case 2, and 52.8% for Case 3, indicating an increase in energy efficiency.Regression analysis was used to examine the data reliability of the scenarios.The R 2 value for η is 0.9609, and 0.9587 for SECTT, and the equations are shown in the graph.Consequently, numerous similar energy efficiency studies of dairy companies have been conducted, and a few are included in this study.Lincoln et al. [54] achieved energy savings of 21% in process integration and electrification for efficient milk-evaporation systems.Buhler et al. [56] reduced the electricity input by 48% and heat rate by 35% in the electrification scenario.This case study is consistent with past research, and it has been demonstrated that energy efficiency would grow in various situations owing to optimization approaches in dairy plants.

CONCLUSIONS
In this study, because of the thermal and electrical energy, the energy consumption of dairy milk factories has a considerable impact on the energy efficiency of applications.The dairy plant's total estimated energy consumption exhibits a relationship between the specific energy consumption and the target specific energy consumption for the energy consumption in each of the three case studies.The target-specific energy consumption was computed to be 6,352.14[MJ/t] in Case 1, 5,898.67 [MJ/t] in Case 2, and 5,445.21[MJ/t] in Case 3. In this study, Cases 2 and 3 exhibited optimized results in terms of energy efficiency.
The annual thermal (steam boiler) and electrical energy expenditures were obtained, with 315.87 [kW] of thermal (steam) energy and 80.98 [kW] of electrical energy.The total thermal and electric energy was summarized as 396.85 [kW].The factory's annual total electricity usage was determined to be 1,049.55[GJ/y], while its coal consumption was 4,093.63 [GJ/y].Despite the factory's expenditure on thermal and electrical energy, its energy efficiency was determined to be 45.5%.The energy efficiency of the factory was 45.5%, but 54.5% of the waste was due to energy losses, fluid milk, energy consumption, and high waste heat.Optimizing processes and installing a recycling waste heat system can increase the efficiency.The input energy was calculated to be 374.24 [kW] for Case 1,356.33 [kW] for Case 2, and 342.08 [kW] for Case 3. The energy efficiency was assessed to be 48.The dairy factory's capacity was obtained by performing specific energy consumption estimations based on the energy consumption data (heat boiler and electric energy) received from the dairy and product plant over a one-year period.This study demonstrates the performance of a manufacturing facility by providing details on the operation of energy analysis in a dairy milk plant.The use of milk and milk-derived products in the dairy industry results in a high energy density.As a result, determining the factory's energy performance to save energy will increase profitability.
The aim of this study is to make the conclusions more realistic by using dependability analysis of the production and energy data.The study's task is to collect the dairy plant's energy and production performance, as well as to make the energy costs and efficiencies as precise as possible over the course of a A plant's energy performance can be measured by ensuring that the production of milk and goods in the factory is synchronized with the use of energy.

ACKNOWLEDGEMENT
This case study was supported by the Biçenler Dairy Agriculture and Animal Products Industry Trade Limited Company as a subject for energy efficiency and consumption studies.Special thanks to the factory management for their assistance with the factory production and energy data systems.The author also acknowledges their valuable counsel and assistance throughout this study.

Figure 1 .
Figure 1.Production department for milk and products in the factory.

Figure 2 .
Figure 2. A scheme of the production of dairy milk processes.

Figure 3 .
Figure 3.A scheme of the life cycle assessment for the dairy milk plant.

Figure 4 .
Figure 4. ECT and TDP of the dairy milk plant for the case study.

Figure 5 .
Figure 5.The dairy milk plant's ECT, SECT, and SECTT for the case studies.

Figure 6 .
Figure 6.Total energy efficiency of dairy milk factory for 3 case studies.

Table 1 .
Evaluation of experimental equipment's relative error and uncertainty of the result C , (AD/VM) [%]

Table 4 .
Dairy product of the annual actual existing products (process energy) based on worldwide asset evaluation

Table 5 .
Total energy consumption of the dairy facility for 3 case studies ECT is the energy consumption of the dairy milk plant [MJ], is total estimated project of the specific energy consumption [MJ/t], and is total target project of the specific energy consumption [MJ/t]. Note:

Table 6 .
Total energy efficiency of the dairy milk factory for ACP

ACP (annual current production of the dairy milk factory)
Total the mass flow rate input, kg/s Total of the mass flow rate output, kg/s Mass flow of the steam, kg/s Process of work power input, W Total energy of the process output, W Process of total heat transfer input, W Process of total heat transfer output, W Steam heat transfer of the process, W Specific heat capacity, J/kg K of the condense, J/kg Enthalpy of the steam, J/kg Consumption of the current, A