Determinants of Energy Consumption in the Dairy Industry: A Case Study in Poland

Janusz Wojdalski; Przemysław Ligenza; Marta Postuła; Bogdan Dróżdż; Roman Niżnikowski

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Determinants of Energy Consumption in the Dairy Industry: A Case Study in Poland

| 31 dic 2023

Environmental Protection and Natural Resources

Volumen 34 (2023): Edición 4 (December 2023)

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Publicado en línea: 31 dic 2023

Páginas: 69 - 91

DOI: https://doi.org/10.2478/oszn-2023-0017

Palabras clave
dairy industry, dairy plants, energy consumption in production, energy efficiency, production profile, technical equipment, environment

© 2023 Janusz Wojdalski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Energy and water consumption in a dairy plant (detailed explanations in the text)

Indicators of energy consumption per unit of end product in a dairy plant (detailed explanations are in the text)

Figure 3.

The influence of the production profile on electric energy and heat consumption (summer)

Figure 4.

The influence of the production profile on electric energy and heat consumption (winter)

The influence of the installed capacity of electrical devices on electric energy and heat consumption (summer)

The influence of the installed capacity of electrical devices on electric energy and heat consumption (winter)

Variation in the indicators of heat consumption per unit of end product in the different types of dairy plants analysed

Thermal efficiency (EEtherm) in the different types of dairy plants analysed

Variation in the indicators of electric energy consumption per unit of end product in the different types of dairy plants analysed

Electric energy efficiency (EEel) in the different types of dairy plants analysed

Winter increase in heat consumption per unit of end product in the types of dairy plants analysed, relative to the summer season

Technological factors influencing energy demand in selected dairy plants

Dairy plant specification	Average electric energy consumption	Season	Regression equations	Correlation coefficient r	Independent variables’ range of variation
T1 (1) P = 2500 kW K_m= 17.67 kW/1000L Z = 4,657,840 L/month	A_e = 488731.7 kWh/month	S/W	A_e = 38530 + 21.93Z_m	0.917	Z_m: 4,058.9 – 6348.8 m³/month
	W_e = 104.9 kWh/1000L	S/W	W_e = 178.6 - 0.019Z_m	−0.982	Z_m: 4,058.9 – 6348.8 m³/month
T4 (1) P = 5400 kW K_m = 6.9 kW/1000 L Z = 777,626 L/24h	A_e = 1842461 kWh/month	S/W	A_e = 80.184 - 74908Z_m	0.63	Z_m: 21749 – 25541 Mg/month Z₁₁: 3597 – 5653 Mg/month Z₁₂: 4,956 – 6240 Mg/month Z₁₃: 113 – 204 Mg/month Z₁₄: 456 – 782 Mg/month Z₁₅: 2184 – 3023 Mg/month
	A_e = 1842461 kWh/month		A_e = 155.75·Z₁₁+84.68·Z₁₂ - 573.45·Z₁₃–11.36·Z₁₄+232.33·Z₁₅ + 139383.53	0.72
	W_e = 77.0 kWh/1000 L		W_e = 0.0002 Z_m + 72.558	Weak correlation
	W_e = 77.0 kWh/1000 L		W_e = 3∙10⁻³ Z₁₁ + 7∙10⁻³·Z₁₂ - 0.017·Z₁₃ - 0.025·Z₁₄ + 3∙10⁻³·Z₁₆ + 36.5	0.31
	A_c = 17060.39 GJ/month		A_c = 0.0946Z_m + 14799	Weak correlation
	A_c = 17060.39 GJ/month		A_c= −1.49·Z₁₁ - 1.33·Z₁₂ + 3.46·Z₁₃ + 5.84·Z₁₄ + 3.93·Z₁₅ + 16572.40	0.65
	W_c = 0.71 GJ/1000 L		W_c= −3E-05Z_m + 1.3441	0.47
	W_c = 0.71 GJ/1000 L		W_c = −1∙10⁵·Z₁₁ - 3∙10⁵·Z₁₂ + 2∙10⁴·Z₁₃ + 7∙10⁻⁶ ·Z₁₄ + 1∙10⁻⁵ ·Z₁₅ + 0.99	0.83
T4 (2) P = 420 kW K_m = 3.2 kW/1000L Z = 132,765 L/24h	A_e = 101,516 kWh/month	S/W	A_e = 16.039 Z_m + 37632	0.741	Z_m: 2,678.12 – 5,276.36 m³/month Z₁₆: 5,171 – 9,293 dm³/month Z₁₇: 29,191 – 44,207 dm³/month Z₁₈: 24,000 – 299,045 dm³/month Z₁₉: 1,184–3,388 dm³/month Z₂₀: 3,962 – 35,914 kg/month Z₂₁: 10,593 – 13,790 kg/month Z₂₂: 37,806 –187,223 kg/month Z₂₃: 281 – 3079 kg/month
	A_e = 101,516 kWh/month		A_e = 0.17·Z₁₆ + 3.38·Z₁₇ + 0.32·Z₁₈ - 0.70·Z₁₉ + 1.62·Z₂₀ + 0.48·Z₂₁ + 0.08·Z₂₂ - 0.84·Z₂₃ - 15048.70	0.984
	W_e = 25.94 kWh/1000 L		W_e = −0.0025 Z_m + 35.795	0.526
	W_e = 25.94 kWh/1000 L		W_e = 5∙10⁻⁶ ·Z₁₆ - 0.002·Z₁₇ + 4∙10⁻⁶·Z₁₈ - 0.001·Z₁₉ - 0.004·Z₂₀ + 4∙10⁻⁶ ·Z₂₁ + 2∙10⁻⁶·Z₂₂ + 0.002·Z₂₃ + 59.29	0.905
T4 (3) P = 300 kW K_m = 6.6 kW/1000L Z_4d = 181,300 L/96 h	A_e = 10,681 kWh/4d	S/W	A_e = 84.71Z_4d - 4676.82	0.675	Z_4d: 173,430 – 192,500 L/4d
	W_e = 58.87 kWh/m³		W_e = 0.1479Z_4d + 31.75	0.285
	A_c = 56.93 GJ/4d		A_c = 0.6966Z_4d - 69.36	0.719
	W_c = 313.56 MJ/m³		W_c = 2.00 Z_4d - 48.81	0.482

Influence of the daily production profile and the installed capacity of electrical devices on energy consumption in T4 dairy plants (producing only milk, dairy beverages, tvorog, and, in some cases, also butter)

Season	Total installed capacity P [kW]	Average energy consumption		Average energy consumption per unit of end product		Multiple regression equation	Coefficient of determination R²	Range of variation in independent variables
Season	Total installed capacity P [kW]	A_e [kWh /24h]	A_c [GJ/24h]	W_e [kWh/1000 L]	W_c [GJ/1000 L]	Multiple regression equation	Coefficient of determination R²	Range of variation in independent variables
S	50.0 – 2,385.0 (34)	2,374.1	59.305	32.13	1.0882	A_e = 559.42 + 29.374Z₁	0.834	Z₁: 2.5 – 80.6
						A_e =345.63 + 4.420P₁ + 18.787P₂	0.950	P₁: 0.0 –1785.0 P₂: 7.5 –170.0
						A_c = 18.658 + 0.460 Z₁ + 1.766Z₂	0.849	Z₁: 2.5 – 80.6 Z₂: 3.1 – 39.6
W	63.0 – 646.0 (24)	1,081.4	52.366	30.70	1.7502	A_e = 358.08 + 35.352Z₁	0.672	Z₁: 1.0 – 59.1
						A_e = 463.75 + 6.235P₃	0.429	P₃: 6.0 – 250.0
						A_c = −0.121 + 0.531P₁ − 1.899P₇	0.811	P₁: 17.4 – 273.7 P₇: 0.0 – 41.2
						A_c = 30.777 + 1.054Z₁	0.441	Z₁: 1.0 – 59.1

Types of examined dairy plants

Type	Production profile
T1	Milk, dairy beverages, cream, tvorog, cheese, butter, casein, milk powder
T2	Identical to T1 plants, excluding milk powder
T3	Identical to T1 plants, excluding milk, dairy beverages, cream, and tvorog
T4	Milk, dairy beverages, cream, and tvorog only

Literature on energy consumption and environmental performance of dairy plants

Research topic	Authors
Energy consumption (direct) and energy conservation	Budny & Weiss [2000], Baker & McKenzie [2005], Benedetti et al. [2016], Biglia et al. [2015], Boutaghriout et al. [2016], Bühler et al. [2018], Cosgrove et al. [2016], De Lima et al. [2018], Domínguez-Niño et al. [2017], Energy Performance Indicator Report [2001], Fushimi & Fukui [2014], Gugała et al. [2015], IFC – World Bank Group [2007], Herbst & Griffiths [1993], Kalla et al. [2017], Kapela et al. [2015], Marchi et al. [2022], Masanet et al. [2014], Milani et al. [2011], Pradella et al. [2017], Rad & Lewis [2014], Ramirez et al. [2006], Rao [1986], Samarin et al. [2020], Srinivasan et al. [2018], Wardrop Engineering Inc. [1997], WS Atkins [1998], Wojdalski [1991], Xu & Flapper [2009], Xu et al. [2012], Zhang et al. [2018].
Heat recovery	Atkins et al. [2011], Atkins et al. [2010], Jbira et al. [2023]
Energy consumption in wastewater treatment	Kowalczyk & Karp [2005], Dąbrowski & Żyłka [2015], Grala et al. [2010], Kowalczyk & Bąbała [2010], Krzemińska et al. [2013].
Renewable energy	Schnitzer et al. [2007], Coskun et al. [2012]
Cleaner production standards	Bosworth et al. [2000], Honkasalo et al. [2005].
Best available techniques	Bosworth et al. [2000], WS Atkins [2005]
Environmental performance	Burnett & Hansen [2008], Honkasalo [2003], Honkasalo et al. [2005], Maxime et al. [2006], Prasad et al. [2004], Prasad & Pagan [2006], Zielińska-Chmielewska [2020].
Energy efficiency and optimization of selected production processes	Alves et al. [2014], Baker [2005], Bouman et al. [1993], Brush et al. [2011], Dobry et al. [2009], Đurić et al. [2005], Erbay et al. [2015], Gawałek & Wesołowski [2008], Jin & Chen [2009; 2011], Johnson & Langrish [2017], Jokandan et al. [2015], Kaleta & Chojnacka [2009], Kemp [2012], Leszczyńska & Lee [2016], Marks & Gut [2007], Moejes & Van Boxtel [2017], Munir et al. [2016], Murphy et al. [2013], Niamsuwan et al. [2013], Palianechka et al. [2022], Patel et al. [2009], Philipp et al. [2018], Písecký [2005; 2012], Raghavan et al. [2004], Seydel et al. [2006], Sikirica et al. [2003], Sorgüven & Özilgen [2012], Taghizadeh-Tabari et al. [2016], Walmsley et al. [2013], Yildirim & Genc [2017].
Financial and environmental implications of energy management	Aymerich et al. [2015], Chauhan et al. [2006], Dalton et al. [2002], Egas et al. [2021], Geary et al. [2010].
Process integration	Atkins et al. [2010; 2011], Chen et al. [2022], Kapustenko et al. [2008], Jbira et al. [2023], Kemp [2005], Lincoln et al. [2022], Schlossera et al. [2019], Walmsley et al. [2016; 2018].
Pulsed electric field processing	Bendicho et al. [2002], Guerrero-Beltrán et al. [2010], Sepulveda et al. [2005]
Solar energy	Boutaghriout et al. [2013], Camci [2020], Panchal et al. [2018]), Schnitzer et al. [2007]
Electric power substitution/electrification	Bühler et al. [2019]
Life cycle assessment (LCA), carbon footprint (CF), sustainable development standards	Djekič et al. [2014], Egas et al. [2021], Kim et al. [2013], Prakash & Henham [2014], Vasilaki et al. [2016], Vergé et al. [2007], Von Keyserlingk et al. [2013], Walmsley et al. [2015]
Cleaner production and the environmental impact of dairy production	Dvarionienė et al. [2012], Dyer et al. [2008], Komisja Europejska [2010], Nutter et al. [2013], Özbay & Demirer [2007], Patankar et al. [2010], Saunders & Barber [2007]
Cleaning of production equipment	Jeurnink & Brinkman [1994], Mierzejewska & Diakun [2011], Muthukumaran et al. [2004], Piepiórka-Stepuk et al. [2017]

General characteristics of the analysed types of dairy plants in summer and winter

FR1	Type of plant (Summer)				Type of plant (Winter)
FR1	T1	T2	T3	T4	T1	T2	T3	T4
K_p1	0.59 – 3.10	0.36 – 5.66	0.54 – 3.10	0.62 – 2.42	0.32 – 2.90	0.22 – 2.99	0.25 – 2.90	0.40 – 1.16
K_p2	0.35 – 1.33	0.23 – 3.54	0.30 – 2.13	0.14 – 0.82	0.19 – 1.18	0.13 – 2.46	0.13 – 1.35	0.18 – 0.90
U_en1	3.52 – 17.74	3.93 – 40.46	5.30 – 31.67	1.13 – 21.40	4.13 – 17.74	1.16 – 39.55	1.17 – 17.74	2.26 – 13.53
U_en2	2.43 – 12.91	1.26 – 23.75	3.06 – 23.75	0.49 – 8.93	0.70 – 13.16	1.02 – 32.41	1.08 – 11.92	1.31 – 8.77
K_m	3.74 – 13.69	2.63 – 28.79	4.02 – 28.79	0.99 – 24.66	1.76 – 24.66	1.99 – 34.33	1.99 – 32.26	3.38 – 24.64

Influence of the daily production profile and the installed capacity of electrical devices on energy consumption in T3 dairy plants (not producing milk, dairy beverages or tvorog)

Season	Total installed capacity P [kW]	Average energy consumption		Average energy consumption per unit of end product		Multiple regression equation	Coefficient of determination R²	Range of variation in independent variables
Season	Total installed capacity P [kW]	A_e [kWh/24h]	A_c [GJ/24h]	W_e [kWh/1000 L]	W_c [GJ/1000 L]	Multiple regression equation	Coefficient of determination R²	Range of variation in independent variables
S	83.0 – 1,923.0 (22)	3,079.5	148.151	37.83	1.5542	A_e = 953.19 + 29.707Z₄ + 430.898Z₆	0.932	Z₄: 10.0 – 335.8 Z₆: 0 – 22.5
						W_e = 42.633 - 0.481Z₅	0.346	Z₅: 0 – 80.3
						A_c = −36.781 + 0.607P₁ - 0.523P₃ + 1.037P₄ + 0.968P₆ + 5.555P₁₀	0.990	P₁: 30.3 – 574.0 P₃: 0.0 – 320.0 P₄: 0.0 – 592.0 P₆: 0.0 – 58.6 P₁₀: 0.0 – 101.0
						W_c = 1.476 + 0.001P₄ - 0.012P₆+0.013P₁₀	0.662
						A_c = 41.899 - 0.964Z₃ + 2.204Z₄ + 15.219Z₆	0.971	Z₃: 0 – 164.2 Z₄: 10.0 – 335.8 Z₅: 0– 80.3 Z₆: 0 – 22.5
						W_c = 1.6316-0.006Z₃ + 0.003Z₄ - 0.013Z₅	0.608	Z₃: 0 – 164.2 Z₄: 10.0 – 335.8 Z₅: 0– 80.3 Z₆: 0 – 22.5
W	30.3 – 1,938.0 (20)	3,548.8	230.243	42.82	2.9086	A_e = 2133.00 - 85.291Z₄ W_e = 32.375 + 5.757Z₆	0.660 0.770	Z₄: 0 – 53.0 Z₆: 0 – 7.9
						A_c = 1.145 + 0.444P₁ + 0.936P₄ + 1.275P₇	0.985	P₁: 30.3 – 470.0 P₄: 0.0 – 592.0 P₇: 0.0 – 87.0
						W_c = 2.2652 + 0.028P₇	0.627	P₁: 30.3 – 470.0 P₄: 0.0 – 592.0 P₇: 0.0 – 87.0
						A_c = 117.423 + 62.187Z₆	0.399	Z₆: 0 – 7.9
						W_c = 2,3795 + 0.291Z₆	0.563	Z₆: 0 – 7.9

Factors that influence energy consumption in dairy production

Group of factors	Measured parameters	Symbols^*
FR1	Basic operating parameters and process automation	K_m, K_p1, K_p2, N₁, N₂, P, U_en1, U_en2, Z, Z_m, Z_4d
FR2	Production profile^**	Z₁, Z₂, Z₃, Z₄, Z₅, Z₆, Z₇, Z₈, Z₉, Z₁₀, Z₁₁, Z₁₂, Z₁₃, Z₁₄, Z₁₅, Z₁₆, Z₁₇, Z₁₈, Z₁₉, Z₂₀, Z₂₁, Z₂₂, Z₂₃
FR3	Technical equipment	P₁, P₂, P₃, P₄, P₅, P₆, P₇, P₈

Influence of the daily production profile and the installed capacity of electrical devices on energy consumption in T1 dairy plants (producing milk powder)

Season	Total installed capacity P [kW]	Average energy consumption		Average energy consumption per unit of end product		Multiple regression equation	Coefficient of determination R²	Range of variation in independent variables
Season	Total installed capacity P [kW]	A_e [kWh/24h]	A_c [GJ/24h]	W_e [kWh/1000 L]	W_c [GJ/1000 L]	Multiple regression equation	Coefficient of determination R²	Range of variation in independent variables
S	454.0 – 2,421.7 (21)	7,682.1	432.650	41.73	2.3207	A_e = 3944.70 + 37.110 Z₄	0.568	Z₄:19.0 – 335.8
						A_c = 134.293 + 0.546P₂ + 0.760P₄	0.789	P₂: 31.0 – 777.0 P₄: 100.0 – 964.0
						A_c = 140.56 + 2.433Z₄ + 1.977Z₅ + 0.815 Z₈	0.834	Z₄: 19.0 – 335.8 Z₅: 0 – 81.8 Z₈: 0 – 335.8
W	318.0–2,548.0 (19)	6,246.0	396.632	52.70	3.4125	A_e = 4289.00 + 41.401Z₄ for W_c R² < 0.20	0.502	Z₄: 10.4 –282.7
						A_c = 83.369 + 0.530P₂ + 0.644P₄ + 0.857P₁₀ for W_c R² < 0.34	0.846	P₂: 31.0 – 777.0 P₄: 100.0 – 964.0 P₁₀: 0.0 – 358.3
						A_c = 246.86 + 3.168 Z₄	0.546	Z₄: 10.4 – 282.7

Selected indicators and factors for analysing the energy performance of dairy plants

Indicators and factors for analysing energy performance	Unit of measure	Type of plant/production profile/product	Indicators			Source
Indicators and factors for analysing energy performance	Unit of measure	Type of plant/production profile/product	Range according to Fig. 2	Mean	Interval/season	Source
Electric energy consumption	kWh/m³ of raw milk	Total production	W_Z	90	10 – 680	WS Atkins [1998]
	kWh/m³ of milk	Milk powder, cheese, butter, yogurt, dairy desserts		27	-	Budny & Weiss [2000]
		Cheese, tvorog, yogurt, dairy desserts		32	-
		Cheese, butter, yogurt, dairy desserts		-	34 – 52
		Butter, milk powder, yogurt, dairy desserts		-	53 – 57
		Cheese, milk powder, butter		64
		Cheese, butter		65
		Yogurt, dairy desserts, tvorog, butter		66
	kWh/m³ of milk	Cheese	W_T	-	2.9 – 6.6
	kWh/Mg of butter	Butter (continuous method)	W_T	-	12.9 – 17.0
	kWh/Mg of milk	Milk processing output (based a study of three dairy plants)	W_Z	-	30 – 80	Boutaghriou et al. [2016]
	kWh/Mg of end product	Milk powder	W_Z	429.9	377.0 – 482.9	Budny et al. [1984a]
Heat consumption	GJ/m³ of raw milk	Total production	W_Z	2.01	0.12 – 14	WS Atkins [1998]
	MJ/m³ of processed milk	Milk powder, cheese, butter, yogurt, dairy desserts		1348	-	Budny & Weiss [2000]
		Cheese, tvorog, yogurt, dairy desserts		1439	-
		Cheese, butter, yogurt, dairy desserts		-	1583 – 2380
		Butter, milk powder, yogurt, dairy desserts		-	2733 – 3171
		Cheese, milk powder, butter		2882	-
		Cheese, butter		1884	-
		Yogurt, dairy desserts, tvorog, butter		2698	-
	GJ/Mg of evaporated water	Spray-drying	W_A	4.87	3 – 20	Baker & McKenzie [2005]
	GJ/Mg of processed milk	Modern dairy plants with heat recovery systems	W_Z	0.34	-	Bosworth et al. [2001]
		Modern plants where hot water is used as an energy carrier		0.50	-
		Older plants where steam is used as an energy carrier		2.00	-
Total energy consumption	MWh/Mg of milk powder	Milk powder (mechanical vapor recompression, MVR)	W_P	3.08	-	Bühler et al. [2019]
	GJ/Mg of product	Milk powder	W_Z	29.95	26.41–33.49	Budny et al. [1984a]
	MWh/Mg of dry product	Spray-drying of cheese whey	W_A	2.049	-	Domínguez-Niño et al. [2018]
Fuel gas	kWh/Mg of milk	Total production (based on a study of three production plants)	W_Z	-	100 – 220	Boutaghriou et al. [2016]
Total energy consumption	GJ/m³ of processed milk^*	Milk, tvorog (based on a study of a selected dairy plant)	-	3.04	1.93 – 2.47 Summer	Wojdalski et al. [2007]
	GJ/m³ of processed milk^*		-	3.04	3.63 – 4.19 Winter
	GJ/m³ of processed milk^**		W_Z	2.46	1.40 –1.96 Summer
	GJ/m³ of processed milk^**		W_Z	2.46	3.04 – 3.55 Winter
Total energy consumption	kWh/kg of evaporated water	Three-phase evaporator	W_A	0.14	-	Prasad et al. [2004]
Total energy consumption	kWh/kg of evaporated water	Five-phase evaporator	W_A	0.085	-	Prasad et al. [2004]
Total energy consumption expressed in fuel equivalents	kg of fuel equivalent/m³ of processed milk^*	Milk, tvorog (based on a study of a selected dairy plant)	W_Z	104	66 – 84 Summer	Wojdalski et al. [2007]
	kg of fuel equivalent/m³ of processed milk^*			104	124 –143 Winter
	kg of fuel equivalent/m³ of processed milk^**			84	48 – 67 Summer
	kg of fuel equivalent/m³ of processed milk^**			84	103–121 Winter
Energy consumption in wastewater treatment	kWh/kg BOD₅	Total production	W_T	-	0.89 – 3.22	Kowalczyk & Karp [2005]
Energy consumption in wastewater treatment	kWh/m³ of wastewater	Total production	W_T	-	1.94 – 6.32	Kowalczyk & Karp [2005]
SO₂ emissions	kg/m³ of raw milk	Total production	W_Z	1.65	0.05 – 22.4	WS Atkins [1998]
SO₂ emissions	kg/m³ of processed milk	Total production	-	0.83	-	Wojdalski et al. [2007]
NO_x emissions	kg/m³ of raw milk	Total production	W_Z	0.49	0.03 – 6.4	WS Atkins [1998]
NO_x emissions	kg/m³ of processed milk	Total production	-	0.60	-	Wojdalski et al. [2007]
CO₂ emissions	kg/m³ of raw milk	Total production	W_Z	1.97	0.01 – 9.23	WS Atkins [1998]
CO₂ emissions	kg/m³ of processed milk	Total production		1.25	-	Wojdalski et al. [2007]
Carbon footprint of end products	kg CO_2eq/kg of butter	Butter	W_Z	8.1	-	Flysjö et al. [2014]
	kg CO_2eq/kg of milk powder	Milk powder		7.4	-
	kg CO_2eq/kg of cheese	Cheese		6.5	-
	kg CO_2eq/kg of dairy beverage	Dairy beverages		1.2	-
Particulate-matter emissions	kg/m³ of raw milk	Total production		1.3	0.01 –14.4	WS Atkins [1998]
Particulate-matter emissions	kg/m³ of processed milk	Total production	W_Z	1.15	-	Wojdalski et al. [2007]
Black carbon emissions	kg/m³ of raw milk	Total production	W_Z	19	0.01 – 193	WS Atkins [1998]
Black carbon emissions	kg/m³ of processed milk	Total production	-	0.04	-	Wojdalski et al. [2007]

Structure of energy consumption in the analysed dairy plant

Process/operation	[kJ/Mg of milk]					Percent share [%]
Process/operation	Steam	Direct fuel consumption	Refrigeration	Other machines and devices	Total	Percent share [%]
End product storage	-	-	41,868	-	41,868	9.46
Packing	-	3,489	-	-	3,489	0.71
Storage before packing	-	-	20,934	-	20,934	4.73
Deodorissation	5,815	-	-	-	5,815	1.32
Cooling	-	-	19,771	-	19,771	4.47
Homogenisation	-	-	-	2,326	2,326	0.53
Pasteurisation	213,992	-	-	-	213,992	48.30
Separation	-	-	-	41,868	41,868	9.46
Clarification/Standardisation	-	-	-	20,934	20,934	4.73
Reception and Storage	-	30,238	41,868	-	72,106	16.29
Total	219,807	33,727	124,441	65,128	443,103	100

Influence of the daily production profile and the installed capacity of electrical devices on energy consumption in T2 dairy plants (not producing milk powder)

Season	Total installed capacity P [kW]	Average energy consumption		Average energy consumption per unit of end product		Multiple regression equation	Coefficient of determination R²	Range of variation in independent variables
Season	Total installed capacity P [kW]	A_e [kWh/24h]	A_c [GJ/24h]	W_e [kWh/1000 L]	W_c [GJ/1000 L]	Multiple regression equation	Coefficient of determination R²	Range of variation in independent variables
S	83.0 – 2,935.0 (37)	3,289.9	118.909	39.52	1.3607	A_e= 748.34 + 60.1453Z₁ + 39.4929Z₃ + 22.079Z₅ + 75.286Z₈ for W_e R² < 0.32	0.442	Z₁: 0 – 211 Z₃: 0 – 164 Z₅: 0 – 141 Z₈: 0 – 157.5
						A_c= 1.152 + 0.310P₁ + 1.266P₂ + 1.082P₆ for W_c R² < 0.37	0.860	P₁: 24.4 – 1155.1 P₂: 0.0 – 434.0 P₆: 250.0 – 956.0
						A_c= 18.295 + 1.501Z₁ + 1.748Z₂ + 0.990Z₃ +1.006 Z₅ −5.385 Z₇ + 5.265 Z₈	0.830	Z₁: 0 – 211 Z₂: 0 – 73 Z₃: 0 – 164 Z₅: 0 – 141 Z₇: 0 – 44.4 Z₈: 0 – 157.5
W	30.3 – 1,938.0 (24)	2129.3	120.581	39.92	2.4672	A_e = 864.92 + 94.4138Z₂ + 104.032Z₈ for W_e R² < 0.20	0.603	Z₂: 0 – 63.6 Z₈: 0 – 75.6
						A_c = −4.109+0.364P₁ +0.989P₂+ 1.080P₇ + 0.191P₁₀ for W_c R² < 0.33	0.896	P₁: 30.3 – 708.0 P₂: 0.0– 312.0 P₇: 0.0 – 68.0 P₁₀: 0.0 – 377.0
						A_c = 26.259+2.981Z₁ +2.492Z₃ + 15.157Z₇ + 6.733Z₈	0.693	Z₁: 0 – 111.6 Z₃: 0 – 64.2 Z₇: 0 – 9.9 Z₈: 0 – 75.6

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Determinants of Energy Consumption in the Dairy Industry: A Case Study in Poland

Publicado en línea: 31 dic 2023

Páginas: 69 - 91

DOI: https://doi.org/10.2478/oszn-2023-0017

Palabras clave
dairy industry, dairy plants, energy consumption in production, energy efficiency, production profile, technical equipment, environment

© 2023 Janusz Wojdalski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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