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Analysis of energy usage in the production of three selected cassava-based foods in Nigeria
Journal of Food Engineering 82 (2007) 217–226 www.elsevier.com/locate/jfoodeng
Analysis of energy usage in the production of three selected cassava-based foods in Nigeria S.O. Jekayinfa a,*, J.O. Olajide b b
a Department of Agricultural Engineering, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Oyo State, Nigeria Department of Food Science and Engineering, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Oyo State, Nigeria
Received 28 April 2006; received in revised form 31 January 2007; accepted 1 February 2007 Available online 16 February 2007
Abstract A study was conducted in 18 cassava processing mills situated in the southwestern part of Nigeria to investigate the energy utilization pattern in the production of three different cassava products, viz: ‘gari’, cassava flour and cassava starch. Six mills specializing in the production of each of the products were randomly selected for investigation. The computation of energy use was done using the spreadsheet program on Microsoft Excel. Optimization models were developed to minimize the total energy input into each production line. The results of the study showed that the observed energy requirements per tonne of fresh cassava tuber for production of gari, starch and flour were 327.17, 357.35 and 345 MJ, respectively. The study identified the most energy-intensive operations in each production line and concluded from optimization results that the total minimum energy inputs required for the production of gari, cassava starch and cassava flour per tonne of fresh cassava tuber were 290.53, 305.20 and 315.60 MJ, respectively. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Cassava products; Energy requirement; Unit operation; Optimization models
1. Introduction 1.1. Energy use and analysis in food processing Energy and food are major concerns of most of the developing countries such as Nigeria. About 70% of Nigerian population depends on agriculture which contributes more than 40% to the gross national product of the country. With the introduction of high-yielding varieties, intensive cropping systems, increased usage of fertilizers and chemicals, and high level of farm mechanization, the modern agriculture has become energy intensive. As in other industries, rising fuel cost and supply limitations plague every sector of Nigerian agricultural industry and these industries are now, more than ever before sensing the need for energy related research to reduce costs through energy
*
Corresponding author. Tel.: +234 8033942248. E-mail address: [email protected] (S.O. Jekayinfa).
0260-8774/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.jfoodeng.2007.02.003
conservation and prevent possible shut downs consequent to reduced availability of energy resources. Few processing factories have any precise idea of the energy consumption of different production areas and in the absence of detailed internal monitoring, the energy efficiencies of different operations is also usually unknown. Knowledge of energy consumption for each product in a factory is useful for several purposes such as budgeting, evaluation of energy consumption for a given product, forecasting energy requirement in a plant, and for planning plant expansion. A limited number of studies have been reported in literature on the determination of energy contents of field operations. These include a study reported by Chang, Chang, and Kim (1996) involving the development of an energy model and a computer simulation model to assess the requirements of electricity, fuel and labour for rice handling, drying, storage and milling processes in Rice Processing Complex (RPC) in Korea. Harper and Tribelhorn (1995) compared the relative energy costs of village – prepared and country processed
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Nomenclature Pl W G D GD C S F FG R P M
peeling washing grating dewatering grinding chipping sieving frying filtering re-sieving post-grinding mixing
weaning foods, both made from the same ingredients, but processed differently before being used. Palaniappan and Subramanian (1998) analysed the 5-year energy consumption data for 25 tea factories in South India. The variation in energy consumption in killowatt hour per kg of tea made in both CTC and orthodox factories-based on factors such as type of tea produced, production capacity of factories climate, etc., were analysed. They also studied the specific energy consumption for the different processes. The consumption of direct energy from major sources in tea industry in Assam India was studied by Baruah and Bhattacharya (1996). They submitted that a tea garden required an estimated 18,408 MJ/ha of human energy in the first year. Other similar works reported in literature relating to evaluation of energy efficiency in processing industries include cashew-nut processing in Nigeria (Jekayinfa & Bamgboye, 2003, 2006); palm-kernel oil processing in Nigeria (Jekayinfa & Bamgboye, 2004, 2007) rice production in Bangladesh (Islam, Rahman, Saker, Ahduzzaman, & Baqui, 2001) sugar-beet production in Morocco (Mrini, Senhaji, & Pimentel, 2002) and, energy and labour use in Italian agriculture (Pellizzi, 1992). This study was undertaken to investigate the energy use pattern in the selected cassava processing mills in southwestern Nigeria and to develop predictive models that could estimate and optimize the energy demand of each unit operation for different selected cassava products. 1.2. Cassava Cassava (Manihot esculenta Crantz) is a perennial vegetatively propagated shrub commonly cultivated within the lowland tropics. The world production of cassava root increased from 70 million tonnes in 1960 to 154 million tonnes in 1991 (CIAT, 1993). Subsequently, the estimated annual global production of cassava between 1998 and 2001 was 168 million tonnes fresh weight out of which about 70% was produced in Nigeria, Brazil, Thailand, Indonesia and Democratic Republic of Congo (FAO, 2001).
SW ML DR CB Z
starch washing milling drying cake breaking production output of a particular cassava product
Subscript g gari s cassava starch f cassava flour
Cassava is the second most important staple, after maize in terms of calories consumed and a major source of calories for about 40% of the African population (Nweke, 1992). It thus alleviates food crisis in Africa because of its agricultural advantages. The main advantages are higher yield per unit area of land as well as per unit of labour compared to other cereals under similar conditions. It is tolerant to drought and produces on poor soils where other staples fail. Cassava roots are rich in carbohydrates, but other nutrients are in low levels (Cock, 1985). Nigeria still remains the largest cassava producer in the World producing about 35 metric tonnes/annum. Nigeria’s primum inter pares position is mainly due to the distribution of the high yielding, disease resistant varieties. These cassava varieties were developed by the International Institute of Tropical Agriculture (IITA), and other national research institutes like the National Root Crop Research Institute (NRCRI), as well as the Federal Government’s effort in increasing food crops through their various programmes like the National Accelerated Food Production Programme, Operation Feed the Nation, the Agricultural Development Project assisted by IFAD Cassava Multiplication Programme, which according to Oke (2005) could give as high a yield as 80–90 tonnes/ha. One of the major problems associated with cassava is the rapid post harvest deterioration, which renders it unpalatable as food. Initially, deterioration is due to physiological processes, which occur within 2–3 days of harvest, and are subsequently followed by microbial deterioration within 5–7 days (Beeching, Dodge, Moore, & Wenham, 1994). Deterioration necessitates the prompt consumption or processing of cassava soon after harvest. Processing of cassava is necessary for several reasons: it is a means of removing or reducing the potentially toxic cyanogenic glucosides present in the fresh cassava. Processing as a means of preservation yields products that have different characteristics and thus create variety in cassava diets. Numerous methods of processing have been developed for cassava in different parts of the world and these
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uct. Production of one tonne of cassava costs $19.50. The market price of gari is between $54.63 and $78.04 per tonne (Oke, 2005).
methods result in the production of a wide variety of food products (Lancaster, Ingram, Lim, & Coursey, 1982). 1.3. Some traditional staple foods from cassava Flour or starch from roots and tubers, especially cassava are utilized in the preparation of various food gels, snacks and baked goods. Such traditional products from cassava include gari, industrial starch, flour, etc. 1.3.1. Gari Gari is widely known in Nigeria and other West African countries. It is called ‘‘atoukpou” in Bokinafaso (Nweke, 1992). The processing operations involved are shown in Fig. 1. Some of the processing steps such as grating, milling and water expression are mechanized (Igbeka, Griffon, & Jory, 1992; Lancaster et al., 1982). In the Eastern part of Nigeria, palm oil is often added during frying (toasting) operation. Addition of palm oil prevents burning during garifying and it has additional desirable effect of changing the colour of the product to yellow. The average urban consumer prefers gari because it is a pre-cooked food prod-
1.3.2. Cassava starch Cassava starch is the starting point for so many important industrial products such as dextrin, glucose syrup, etc. Cassava starch is preferred amongst other types because of its good gelling property. Traditionally, cassava starch is produced by first washing the peeled root manually and then grating to produce starch milk from which the fiber is separated through special strainers or sieved through muslin cloth and washed thoroughly and the starch will then collect and settle down (Fig. 1). The indicative price of cassava starch in Nigeria is about $456/tonne, with market value of about $22.6million/ annum, leading to the creation of about 150,000 jobs (Oke, 2005). 1.3.3. Cassava flour The utilization of cassava in the production of fast foods would make the urban population attracted to such noo-
Cassava roots
Peel/Wash
Grate
Mill
Chip and grind
Key
Dewater
Material
Bagged Mash
Mix with water 4, 6, 10 day
Unit Operation
Cake breaking
Dry
Mill finely
Ferment 2, 4 and 6 days
Dewater
Pulverise/sift
Roast Cassava flour Gari
Filter
Wash
Dewater
Dry
Mill
Starch
Fig. 1. Processing steps for the selected cassava-based products.
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dles, breakfast cereals, pies, etc. Presently in Nigeria, there is a regulation that all wheat flour should contain 10% cassava flour and this presently requires 200,000 tonnes of cassava flour in which only about 10,000 tonnes have been supplied. One of the important reasons responsible for this is the standard required to the cassava flour which is high and cannot be compromised (Oke, 2005). The estimated market value of cassava flour in Nigeria is $25million/annum with an employment potential of about 800,000 jobs at two people/tonne. To be able to satisfy this demand, about 400 small scale factories producing 2 tonnes of cassava flour per day are needed (Oke, 2005). 2. Materials and methods Processing data for the three products were gathered by direct measurement during the normal operation period of six randomly selected mills specializing in the production of each selected cassava-based food. The random selection was done from the pool of mills that were not only up to date in their data management practice but also less than 10 years old to ensure that they were within their useful years. The areas of study covered Osun and Oyo States of Nigeria. Osun and Oyo States cover an area of 17,600 sq. km and have a total population of about 5 million people (1991 census), with a population density of about 284 people per sq. km. The energy analysis was based on process analysis in which the direct energy consumption in every successive production step was estimated and the materials input to each operation also indicated. The principal operations involved in the production of each selected cassava-based food are highlighted in Fig. 1. The estimation of thermal energy (obtained from the use of fuel), electrical energy (obtained from electricity use from the national grid) and manual energy (from human labour) was done as follows (Jekayinfa & Bamgboye, 2004, 2006, 2007). 2.1. Evaluation of electrical energy The rated horsepower of each motor was multiplied by the corresponding hours of operation and summed to find the electrical energy usage by equipment. A motor efficiency of 80% was assumed to compute the electrical inputs (Johnson, 1999; Rajput, 2001). 2.2. Evaluation of thermal energy Energy from fossil fuel was assigned to each unit operation according to their level of consumption. The total quantity of energy consumed from fossil fuel was converted to common energy unit (Joule) by multiplying the quantity of fuel consumed by the corresponding calorific value (lower heating value) of the fuel used (Ezeike, 1981; Johnson, 1999; Rajput, 2001).
2.3. Evaluation of manual energy Manual energy was estimated based on the value recommended by Odigboh (1997). According to Odigboh (1997), at the maximum continuous energy consumption rate of 0.30 kW and conversion efficiency of 25%, the physical power output of a normal human labour in tropical climates is approximately 0.075 kW sustained for an 8–10 h work day. All other factors affecting manual energy expenditure were found insignificant and therefore neglected. To determine the manual energy input for a given operation, the time spent by the worker on each operation was recorded. This included the intermittent resting periods. For any unit operation, the manual energy expenditure, Em, was determined by Em ¼ 0:075 N T a
ðkWhÞ
ð1Þ
where 0.075 = the average power of a normal human labour in kW N = number of persons involved in an operation Ta = useful time spent to accomplish a given task (operation), h To access the energy demands (electrical, fuel or manual) in all the eight unit operations of cassava-based food production, quantitative data on operating conditions were measured. The energy consumed in all the unit operations involved in the production of each of the selected cassavabased products was measured and a series of equations were developed for each of the unit operations. 2.4. Experimental procedure Before the commencement of the experiments, known quantity of fuel was measured into the empty tank of the captive electricity generator in each mill. The initial reading of the electric power-reading meter installed in each section of the mill was taken at this time. After the completion of the processing of 1000 kg of raw cassava into any of the selected three products, the quantity of the fuel left in the generator’s tank and the reading of the electric meter, installed for this purpose, were taken. The differences in these readings represented the quantity of fuel used (in litres) and the electric power consumed in (kW), respectively. For each of the operations, the number of persons involved was counted and the time taken was also recorded using a stopwatch with all intermittent resting and idle period deducted. From this procedure it was possible to assign thermal, electrical, both thermal and electrical, or manual energy, as the case may be, to each unit operation. Conversion of these raw data to energy equivalent was done using the developed energy equations. The processing facilities of all the selected mills are very similar. All the mills selected were evaluated over the same period and seasons, and as a result, the error of seasonal
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slight modifications, energy and mass flow diagrams (Figs. 2–4) were constructed for a typical gari, starch and flour mills, respectively. Various models (linear, semi-log, log-linear and polynomial) were tried to develop the functional relationship between energy input and food productivity to optimize the food production system. The selection of appropriate model for a particular situation was made on the basis of R2 value. The general equation fitted for different cassava-based foods was of the following nature (Sidhu, Singh, Singh, & Ahuja, 2001): m X n X i Aij ðxj Þi ð2Þ y ¼ A0 þ
changes was eliminated. No prior experimental conditions were used as data collection in each locality was done as the mills were in operation. The apparatus used for the study include: (i) A stop watch for measuring production time. (ii) A measuring cylinder for quantifying the amount of fuel consumed during each unit operation. For consistency, the energy components were calculated on the basis of 1000 kg of raw cassava. This approach is similar to that used in previous studies by Ezeike (1981) and Jekayinfa and Bamgboye (2003, 2004, 2007). Using energy accounting symbols presented by Singh (1978) with
i¼1
j¼1
Cassava 1000
28.13
Peeling peels 9.0
120
Washing 223.38
8.25
0.59
Grating
0.94
Dewatering Water
7.50
600
Sieving
Mass flow (kg) Electrical energy (MJ)
Manual energy (MJ) 485
Fibres
22.38
Thermal energy (MJ) 10.50
15
Frying
Unit operation
1.22
Re-sieving
0.30
Garilumps
14.60
Post-grinding
Fine textured Gari 250
Fig. 2. Energy flow diagram in a typical gari processing mill.
15
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Cassava 1000
28.13
Peeling Peels 9.0
120
Washing 223.75
8.25
0.59
0.21
Chipping
80.55
2.92 Grinding
1.20
Mixing
Mass flow (kg) Electrical energy (MJ) Thermal energy (MJ)
1.20
Filtering
Manual energy (MJ) Unit operation 0.60
Starch Washing
0.94
Dewatering Water
380
Starch 500
Fig. 3. Energy flow diagram in a typical starch producing mill.
where m is equal to 1 for linear relationship and 2 for quadratic relationship; y, yield, kg/tonne of cassava; A0, intercept; n, number of independent variables; Aij, regression coefficients; and xj is the independent variable. The sensitivities of the apparatus used in the course of this study and the error analysis were calculated . 3. Results and discussion 3.1. Energy requirement for gari processing operations Average energy inputs at different stages of production of gari are presented in Table 1 and Fig. 2. From Table 1 and Fig. 2, it was observed that in all the gari processing
mills investigated; thermal energy is mostly used, followed by manual energy and electrical energy. This shows that majority of the mills depend on fuel for operations. 75.2% of the average total energy in all the gari mills was obtained from thermal source, followed by 17.8% and 7.0% obtained from manual and electrical energy sources, respectively. This evidently shows that most of the tedious operations involved in gari processing are actually carried out mechanically with over 80% of energy consumption attributed to either the use of internal combustion engine or electric motors for operating processing machines. Considering the unit operations during gari production (Fig. 2), it was observed that all the unit operations
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Cassava 1000
28.13
Peeling Peeks 9.0
120
Washing 223.75
8.25
0.59
Mass flow (kg)
Grating
0.90
Dewatering
Electrical energy (MJ) Thermal energy (MJ)
Water
0.60
Manual energy (MJ)
600
Cake Breaking
Unit operation 0.60
Drying 67.13 0.15
7.30
Water
80
Milling
Cassava flour 200
Fig. 4. Energy flow diagram in a typical cassava flour producing mill.
required manual energy in different quantity. Only grating utilized all the available energy sources. The average energy use for grating (232.22 MJ) was the highest accounting for Table 1 Estimates of energy input by unit operations for production of ‘‘gari” from 1000 kg of cassava tubers Unit operation
Time (h)
Energy (MJ) Electrical
Thermal
Manual
Total
Percent of total
Peeling Washing Grating Dewatering Sieving Frying Re-sieving Post grinding Total Percent of total
25.00 8.00 1.13 2.50 2.00 2.00 3.25 2.00
– – 8.25 – – – – 14.60
– – 223.38 – – 22.38 – –
28.13 9.00 0.59 0.94 7.50 10.50 1.22 0.30
28.13 9.00 232.22 0.94 7.50 32.88 1.22 14.90
8.60 2.75 71.10 0.29 2.30 10.06 0.37 4.60
45.88
22.85 7.00
245.76 75.20
58.18 17.80
326.79 100.00
100.00
71.1% of the total energy consumption. This was followed by frying (32.88 MJ, 10.06%), peeling (28.13 MJ, 8.6%) and post grinding (4.9 MJ, 4.6%). Other results include washing (9.0 MJ, 2.75%), sieving (7.5 MJ, 2.3%), re-sieving (1.22 MJ, 0.37%) and dewatering (0.94 MJ, 0.29%). In all, the total energy requirement for processing 1000 kg of cassava tuber into ‘gari’ is 326.79 MJ. The mean value of errors between the measured value and the true value was 0.152. The standard deviation of the differences in the 6 mills was 0.174 with a worst-case error of 0.03. 3.2. Energy requirement for production of cassava starch Table 2 and Fig. 3 show the average energy consumption on the basis of all unit operations involved in cassava starch production in the study area. Eight readily defined unit operations common to all starch mills visited are: cassava peeling, washing, chipping, grinding, mixing, filtering, starch washing and dewatering. From Table 2, it can be
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Table 2 Estimates of energy input by unit operations for the production of cassava starch from 1000 kg of cassava tubers
Table 3 Estimates of energy input by unit operations for the production of cassava flour from 1000 kg of cassava tubers
Unit operation
Time (h)
Energy (MJ) Thermal
Manual
Total
Unit operation
Time (h)
Energy (MJ)
Electrical
Electrical
Thermal
Manual
Total
Peeling Washing Chipping Grinding Mixing Filtering Starch washing Dewatering Total Percent of total
25.00 8.00 1.13 0.40 4.00 4.00 2.00
– – 8.25 2.92 – – –
– – 223.75 80.55 – – –
28.13 9.00 0.59 0.21 1.20 1.20 0.60
28.13 9.00 232.59 83.68 1.20 1.20 0.60
Peeling Washing Grating Dewatering Cake breaking Drying Milling Total Percent of total
25.00 8.00 1.13 0.40 2.00
– – 8.25 – –
– – 223.75 – –
28.13 9.00 0.59 0.90 0.60
28.13 9.00 232.59 0.90 0.60
8.12 2.60 67.15 0.26 0.17
4.00 1.00 41.53
– 7.30 15.55 4.49
–
0.60 0.15 39.97 11.54
0.60 74.58 346.40 100.00
0.17 21.53 100.00
2.50 47.03
– 11.17 3.13
– 304.30 85.15
0.94 41.87 11.72
0.94 357.34 100.00
Percent of total 7.87 2.52 65.09 23.42 0.34 0.34 0.17 0.26 100.00
observed that thermal energy accounted for more than 85% of the total energy consumption in all the mills. 11.72% and 3.13% of the total energy used could be attributed to manual and electrical energy sources, respectively. This again shows that the most tedious and energy consuming operations (chipping and grinding) were performed mechanically by the use of machines powered by either an electric motor or internal combustion engine. As depicted by Table 2 and Fig. 3, only two operations (chipping and grinding) combined the use of all available energy sources. Other operations that were performed entirely manually are peeling, washing, mixing, filtering, starch washing and dewatering with average energy requirements of 28.13, 9.0, 1.20, 1.20, 0.60, and 0.94 MJ, respectively. Conclusively, the average total energy requirement for producing cassava starch in all the mills investigated is 357.34 MJ per 1000 kg of cassava tubers, with the mean value of errors of 0.158 and standard deviation of 0.185. 3.3. Energy requirement for production of cassava flour Energy consumption pattern in the production of cassava flour from selected typical mills in the study area is presented in Table 3 and Fig. 4. Production of flour comprises 7 readily defined unit operations viz: Peeling, washing, grating, dewatering, cake breaking, drying, and milling. The average total energy consumption per 1000 kg of raw cassava tubers for all these 7 unit operations in the selected cassava flour mills was 346.40 MJ. The mean value of errors between the measured values and true value was 0.165. The standard deviation of the differences in the 6 mills was 0.158 with a worst-case error of 0.03. The two prominent energy intensive operations are grating and milling, accounting for 67.15% and 21.53% of the total energy, respectively. Other operations with their percent energy contributions in parenthesis are peeling (8.12%), washing (2.60%) and cake breaking (0.17%). As with other 2 cassava products, thermal energy was the mostly used energy source with average total contribution of 83.97% of the total energy consumed. This was followed
67.13 290.88 83.97
Percent of total
by manual energy and electrical energy with percent contributions of 11.54% and 4.49%, respectively. As indicated in Fig. 4, cassava conversion rate in a typical flour-producing mill is 20%. It could be observed in all the processing mills visited that thermal energy, from the use of fuel, was prominent. This is due to incessant failure in power supply from the national grid during the period this study lasted. 3.4. Optimization equations Eqs. (3)–(5) are the optimization equations developed for the selected cassava-based foods from the raw energy data collected on the basis of different unit operations. The production output of different products under study was optimized by multi-variable technique with no constraints with respect to various energy inputs. The technique enables the maximum production output achieved and the optimum value of each unit operation to be calculated for each product. Table 4 is the summary of data collected on observed and optimum values for product output and energy inputs from different unit operations. 3.4.1. Gari Both linear and quadratic relationships gave significant correlation for gari production. On the basis of R2 value of 0.85, which is the higher value, linear model was selected as given in Eq. (3) Y g ¼ 25Plg þ 8W g þ 1:3Gg þ 2:5Dg þ 20S g þ 20F g þ 3:2Rg þ 2P g
ð3Þ
Variation explained by this relationship was 85% and the remaining 15% variation might be due to unforeseen and sometimes, unexplainable parameters, such as operators’ ages and experiences, equipment types and age, management practices, maturity and cultivars of cassava tuber being processed, etc. All unit operations involved in gari production contributed significantly to energy consumption. The most significant unit-operation in the developed model was grating.
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Table 4 Comparison of observed and optimum values for product output and energy inputs from different unit operations for gari, cassava starch and cassava flour Unit operation
Peeling Washing Grating Chipping Grinding Dewatering Mixing Sieving Cake breaking Filtering Frying Drying Milling Re-sieving Starch washing Post grinding Total energy (MJ) Yield (kg) a b
Gari
Cassava starch
Cassava flour
Obs. V. (MJ)a
Opt. V. (MJ)b
Change (%)
Obs. V. (MJ)
Opt. V. (MJ)
Change (%)
Obs. V. (MJ)
Opt. V. (MJ)
Change (%)
28.13 9.00 232.22 – – 0.94 – 7.50 – – 32.88 – – 1.22 – 14.90 326.79
20.15 7.00 217.92 – – 0.74 – 7.00 – – 28.50 – – 1.02 – 11.50 293.83
28.37 22.22 6.16 – – 21.28 – 6.67 – – 13.32 – – -16.40 – 22.82 10.09
28.13 9.00 – 232.59 83.68 0.94 1.20 – – 1.20 – – – – 0.60 – 357.34
18.15 7.10 – 205.00 71.81 0.74 1.00 – – 1.00 – – – – 0.40 – 305.20
35.48 21.11 – 11.87 14.09 21.28 16.62 – – 16.67 – – – – 33.33 – 14.59
28.13 9.00 232.59 – – 0.90 – – 0.60 – – 0.60 74.58 – – – 346.40
20.13 7.10 216.90 – – 0.90 – – 0.45 – – 0.60 70.38 – – – 315
28.44 21.11 6.75 – – – – – 25.00 – – – 5.63 – – – 8.85
250.00
325.00
30.00
500
585.00
+17.00
200
230
+15.00
Obs. V. – Observed value Opt. V. – Optimized value
Gari yield was maximized for the optimum level of different energy inputs. The maximized yield value was estimated to be 325 kg of gari/tonne of cassava tuber for the total energy inputs level of 293.83 MJ. The input energy as can be seen in Table 1 and Figs. 1 and 2 consisted of peeling, washing, grating, dewatering, sieving, frying, reseiving and post grinding. The optimum level in peeling operation can be achieved by using more energy per tonne of raw cassava tuber to be peeled. This can be achieved by increasing the number of persons involved in peeling. Also, optimal levels can be achieved for washing, dewatering and frying operations by the same method of increasing the energy per materials to be processed. Grating, seiving, reseiving and post grinding are also very critical operations in gari production in respect to timeliness. Therefore to achieve optimal levels of energy input, one could make use of more efficient and tested equipment for each of the operations.
connected to unforeseen parameters such as mentioned in the preceeding subsection. All the energy inputs have significant contributions to the model. Cassava starch yield was also maximized for optimum level of various energy inputs. The maximized cassava starch output was estimated to be 585 kg/tonne of raw cassava tuber for the total energy inputs level of 305.20 MJ. The energy inputs for cassava starch production were from peeling, washing, chipping, grinding, mixing, filtering, starch washing and dewatering as presented in Table 2 and Figs. 1 and 3. The comparison of observed and optimum value for various energy inputs was also presented in Table 4.
3.4.2. Cassava starch The yield of cassava starch was best explained by linear equation having tested all other models based on the R2 value. The relationship between cassava starch yield and various energy inputs is given by Eq. (4). The R2 value for the equation was 0.88
Y f ¼ 25Plf þ 8W f þ 1:13Gf þ 0:4Df þ 2CBf þ MLf
Y s ¼ 25Pls þ 8W s þ 1:3C s þ 2:5GDs þ 20M s þ 20F s þ 3:25S s þ 2Ds
ð4Þ
As observed in the case of gari, the remaining 12% variation not explained by the relationship in Eq. (4) could be
3.4.3. Cassava flour The 84% variation in yield of cassava flour was significantly explained by a linear equation (Eq. (5)) with R2 value equal to 0.84 ð5Þ
Yield of cassava flour was also maximized with multivariable technique with no constraints. The estimated yield of cassava flour from the developed equation was 230 kg/ tonne of raw cassava tuber for 315.6 MJ of total energy input from unit operations consisting of peeling, washing, grating, dewatering, cake breaking, drying and milling. Similar to the observation in the two previously discussed products, energy consumption in manual operations such as peeling, washing, dewatering, cake breaking and drying could be optimized by carefully deciding on the number of
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persons that could be involved in these operations on the basis of available work place thereby reducing time of operation, increasing production output and reducing unit cost of production. The energy inputs for grating and milling can be optimized by using high-capacity motorized grater and attrition mill, respectively. 4. Conclusions The following conclusions can be drawn from the results of this study: The observed energy requirements per 1000 kg of fresh cassava tuber for production of gari, starch and flour are 327.17 MJ, 357.35 MJ and 345 MJ, respectively. The study identified the most energy-intensive operations in each production line and concluded from optimization results that the total minimum energy inputs required for the production of gari, cassava starch and cassava flour per tonne of fresh cassava tuber were 290.53, 305.20 and 315.60 MJ, respectively. The energy consumption for all manual operations in all selected production lines could be optimized by carefully deciding on the number of persons that could be involved in these operations on the basis of available work place thereby reducing time of operation, increasing production output and reducing unit cost of production. The energy use in mechanized operations could be optimized by using efficient and high-capacity processing machines, for example, IITA (International Institute of Tropical Agriculture) developed cassava processing equipment.
References Baruah, D. C., & Bhattacharya, P. C. (1996). Energy utilization pattern in the manufacture of black tea. Journal of Agricultural Mechanization in Asia, Africa and Latin America, 27(4), 65–70. Beeching, J. R., Dodge, A. D., Moore, K. G. & Wenham, J. E. (1994). Physiological deterioration in cassava: an incomplete wound response? The Cassava Biotechnology Network. In A. M. Thro & W. R. Roca (Eds.), Proceedings of the second international scientific meeting, 22–26 August, Cali, Columbia, CIAT 1995 (pp. 729–736). Chang, H. H., Chang, D. I., & Kim, D. C. (1996). Energy model of rice processing complex. American Society of Agricultural Engineers (ASAE). Paper No. 946064, St. Joseph, USA, 34pp. CIAT, (1993). Cassava: the latest facts about an ancient crop. Centro International de Agricultural Tropical, Apartedo Aereo 6713 Cali, Colombia, pp 9. Cock, J. H. (1985). Cassava: new potential for a neglected crop. Colorado, USA: West View Press (pp. 74–91).
Ezeike, G. O. I. (1981). Energy consumption in rice processing operations in Nigeria: selected case studies. Journal of Agricultural Mechanization in Asia, Africa and Latin America, 18(1), 33–40. FAO, (2001). FAOSTAT Agricultural data (2000). Food and Agriculture Organization of the United Nations, Rome, 2001. http://www.fao.org/ forestry/fo/fra/index.jsp. Harper, J. M., & Tribelhorn, R. E. (1995). Comparison of relative energy costs of village-prepared and centrally processed weaning foods. Food and Nutrition Bulletin (UN), 7(4), 54–60. Igbeka, J. C., Griffon, D., & Jory, M. (1992). Selective mechanization for cassava processing. Agricultural Mechanization in Asia, Africa and Latin America, 23(1), 45–50. Islam, A. S., Rahman, M. A., Saker, R. I., Ahduzzaman, M., & Baqui, M. A. (2001). Energy audit for rice production under power filler and bullock farming systems in Bangladesh. Agricultural Mechanization in Asia, Africa and Latin America, 31(2), 873–876. Jekayinfa, S. O. & Bamgboye, A. I. (2003). An analysis of energy use patterns in Nigeria’s industrial sector: a case study of a cashew nut processing plant. In Proceedings of the 4th international conference of the Nigerian institution of agricultural engineers, Vol. 25 (pp. 70–77). Jekayinfa, S. O., & Bamgboye, A. I. (2004). Energy requirements for palm kernel oil processing operations. Nutrition & Food Science, 34(4), 166–173. Jekayinfa, S. O., & Bamgboye, A. I. (2006). Estimating energy requirements in cashew nut processing operations. Energy, 31(8–9), 1305–1320. Jekayinfa, S. O., & Bamgboye, A. I. (2007). Development of equations for estimating energy requirements in palm-kernel oil processing operations. Journal of Food Engineering, 79(1), 322–329. Johnson, R. A. (1999). Miller and Freund’s probability and statistics for engineers (5th ed.). New Delhi: Prentice-Hall of India Private Ltd. Lancaster, P. A., Ingram, J. S., Lim, M. Y., & Coursey, D. G. (1982). Traditional cassava based foods survey of processing technique. Econ. Bot., 36(1), 12–45. Mrini, M., Senhaji, F., & Pimentel, D. (2002). Energy analysis of sugar beet production under traditional and intensive farming systems and impacts on sustainable agriculture in Moroco. Journal of Sustainable Agriculture, 20(4), 5–28. Nweke, F. (1992). Processing potential for cassava production growth in Africa. COSCA Working Paper no. 11. IITA, Ibadan, Nigeria. Odigboh, E. U. (1997). Machines for crop production. In B. A. Stout (Ed.), Handbook of agricultural engineering – Plant production engineering. American Society of Agricultural Engineers. Oke, O. L. (2005). Cassava; yesterday, today and tomorrow. Personal communication, Ife Lecture Series No. 7 of Obafemi Awolowo University. The Postgraduate College Thursday, 23 June, 2005. Palaniappan, C., & Subramanian, S. V. (1998). A study and analysis of energy consumption patterns in tea factories of south India for energy conservation solutions. Journal of Agricultural Mechanization in Asia, Africa and Latin America, 29(2), 12–16. Pellizzi, G. (1992). Use of energy and labour in Italian Agriculture. Journal of Agricultural Engineering Research, 52, 111–119. Rajput, R. K. (2001). Thermal engineering. New Delhi: Laxmi Publications (P) Ltd. (pp. 434–464). Sidhu, H. S., Singh, S., Singh, T., & Ahuja, S. S. (2001). Optimization of energy usage in different crop production. Journal of Indian Institute of Engineers (Agricultural Engineering Division), 85, 1–4. Singh, R. P. (1978). Energy accounting in food process operations. Food Technology, 32(4), 40–46.
Analysis of energy usage in the production of three selected cassava-based foods in Nigeria S.O. Jekayinfa a,*, J.O. Olajide b b
a Department of Agricultural Engineering, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Oyo State, Nigeria Department of Food Science and Engineering, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Oyo State, Nigeria
Received 28 April 2006; received in revised form 31 January 2007; accepted 1 February 2007 Available online 16 February 2007
Abstract A study was conducted in 18 cassava processing mills situated in the southwestern part of Nigeria to investigate the energy utilization pattern in the production of three different cassava products, viz: ‘gari’, cassava flour and cassava starch. Six mills specializing in the production of each of the products were randomly selected for investigation. The computation of energy use was done using the spreadsheet program on Microsoft Excel. Optimization models were developed to minimize the total energy input into each production line. The results of the study showed that the observed energy requirements per tonne of fresh cassava tuber for production of gari, starch and flour were 327.17, 357.35 and 345 MJ, respectively. The study identified the most energy-intensive operations in each production line and concluded from optimization results that the total minimum energy inputs required for the production of gari, cassava starch and cassava flour per tonne of fresh cassava tuber were 290.53, 305.20 and 315.60 MJ, respectively. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Cassava products; Energy requirement; Unit operation; Optimization models
1. Introduction 1.1. Energy use and analysis in food processing Energy and food are major concerns of most of the developing countries such as Nigeria. About 70% of Nigerian population depends on agriculture which contributes more than 40% to the gross national product of the country. With the introduction of high-yielding varieties, intensive cropping systems, increased usage of fertilizers and chemicals, and high level of farm mechanization, the modern agriculture has become energy intensive. As in other industries, rising fuel cost and supply limitations plague every sector of Nigerian agricultural industry and these industries are now, more than ever before sensing the need for energy related research to reduce costs through energy
*
Corresponding author. Tel.: +234 8033942248. E-mail address: [email protected] (S.O. Jekayinfa).
0260-8774/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.jfoodeng.2007.02.003
conservation and prevent possible shut downs consequent to reduced availability of energy resources. Few processing factories have any precise idea of the energy consumption of different production areas and in the absence of detailed internal monitoring, the energy efficiencies of different operations is also usually unknown. Knowledge of energy consumption for each product in a factory is useful for several purposes such as budgeting, evaluation of energy consumption for a given product, forecasting energy requirement in a plant, and for planning plant expansion. A limited number of studies have been reported in literature on the determination of energy contents of field operations. These include a study reported by Chang, Chang, and Kim (1996) involving the development of an energy model and a computer simulation model to assess the requirements of electricity, fuel and labour for rice handling, drying, storage and milling processes in Rice Processing Complex (RPC) in Korea. Harper and Tribelhorn (1995) compared the relative energy costs of village – prepared and country processed
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Nomenclature Pl W G D GD C S F FG R P M
peeling washing grating dewatering grinding chipping sieving frying filtering re-sieving post-grinding mixing
weaning foods, both made from the same ingredients, but processed differently before being used. Palaniappan and Subramanian (1998) analysed the 5-year energy consumption data for 25 tea factories in South India. The variation in energy consumption in killowatt hour per kg of tea made in both CTC and orthodox factories-based on factors such as type of tea produced, production capacity of factories climate, etc., were analysed. They also studied the specific energy consumption for the different processes. The consumption of direct energy from major sources in tea industry in Assam India was studied by Baruah and Bhattacharya (1996). They submitted that a tea garden required an estimated 18,408 MJ/ha of human energy in the first year. Other similar works reported in literature relating to evaluation of energy efficiency in processing industries include cashew-nut processing in Nigeria (Jekayinfa & Bamgboye, 2003, 2006); palm-kernel oil processing in Nigeria (Jekayinfa & Bamgboye, 2004, 2007) rice production in Bangladesh (Islam, Rahman, Saker, Ahduzzaman, & Baqui, 2001) sugar-beet production in Morocco (Mrini, Senhaji, & Pimentel, 2002) and, energy and labour use in Italian agriculture (Pellizzi, 1992). This study was undertaken to investigate the energy use pattern in the selected cassava processing mills in southwestern Nigeria and to develop predictive models that could estimate and optimize the energy demand of each unit operation for different selected cassava products. 1.2. Cassava Cassava (Manihot esculenta Crantz) is a perennial vegetatively propagated shrub commonly cultivated within the lowland tropics. The world production of cassava root increased from 70 million tonnes in 1960 to 154 million tonnes in 1991 (CIAT, 1993). Subsequently, the estimated annual global production of cassava between 1998 and 2001 was 168 million tonnes fresh weight out of which about 70% was produced in Nigeria, Brazil, Thailand, Indonesia and Democratic Republic of Congo (FAO, 2001).
SW ML DR CB Z
starch washing milling drying cake breaking production output of a particular cassava product
Subscript g gari s cassava starch f cassava flour
Cassava is the second most important staple, after maize in terms of calories consumed and a major source of calories for about 40% of the African population (Nweke, 1992). It thus alleviates food crisis in Africa because of its agricultural advantages. The main advantages are higher yield per unit area of land as well as per unit of labour compared to other cereals under similar conditions. It is tolerant to drought and produces on poor soils where other staples fail. Cassava roots are rich in carbohydrates, but other nutrients are in low levels (Cock, 1985). Nigeria still remains the largest cassava producer in the World producing about 35 metric tonnes/annum. Nigeria’s primum inter pares position is mainly due to the distribution of the high yielding, disease resistant varieties. These cassava varieties were developed by the International Institute of Tropical Agriculture (IITA), and other national research institutes like the National Root Crop Research Institute (NRCRI), as well as the Federal Government’s effort in increasing food crops through their various programmes like the National Accelerated Food Production Programme, Operation Feed the Nation, the Agricultural Development Project assisted by IFAD Cassava Multiplication Programme, which according to Oke (2005) could give as high a yield as 80–90 tonnes/ha. One of the major problems associated with cassava is the rapid post harvest deterioration, which renders it unpalatable as food. Initially, deterioration is due to physiological processes, which occur within 2–3 days of harvest, and are subsequently followed by microbial deterioration within 5–7 days (Beeching, Dodge, Moore, & Wenham, 1994). Deterioration necessitates the prompt consumption or processing of cassava soon after harvest. Processing of cassava is necessary for several reasons: it is a means of removing or reducing the potentially toxic cyanogenic glucosides present in the fresh cassava. Processing as a means of preservation yields products that have different characteristics and thus create variety in cassava diets. Numerous methods of processing have been developed for cassava in different parts of the world and these
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219
uct. Production of one tonne of cassava costs $19.50. The market price of gari is between $54.63 and $78.04 per tonne (Oke, 2005).
methods result in the production of a wide variety of food products (Lancaster, Ingram, Lim, & Coursey, 1982). 1.3. Some traditional staple foods from cassava Flour or starch from roots and tubers, especially cassava are utilized in the preparation of various food gels, snacks and baked goods. Such traditional products from cassava include gari, industrial starch, flour, etc. 1.3.1. Gari Gari is widely known in Nigeria and other West African countries. It is called ‘‘atoukpou” in Bokinafaso (Nweke, 1992). The processing operations involved are shown in Fig. 1. Some of the processing steps such as grating, milling and water expression are mechanized (Igbeka, Griffon, & Jory, 1992; Lancaster et al., 1982). In the Eastern part of Nigeria, palm oil is often added during frying (toasting) operation. Addition of palm oil prevents burning during garifying and it has additional desirable effect of changing the colour of the product to yellow. The average urban consumer prefers gari because it is a pre-cooked food prod-
1.3.2. Cassava starch Cassava starch is the starting point for so many important industrial products such as dextrin, glucose syrup, etc. Cassava starch is preferred amongst other types because of its good gelling property. Traditionally, cassava starch is produced by first washing the peeled root manually and then grating to produce starch milk from which the fiber is separated through special strainers or sieved through muslin cloth and washed thoroughly and the starch will then collect and settle down (Fig. 1). The indicative price of cassava starch in Nigeria is about $456/tonne, with market value of about $22.6million/ annum, leading to the creation of about 150,000 jobs (Oke, 2005). 1.3.3. Cassava flour The utilization of cassava in the production of fast foods would make the urban population attracted to such noo-
Cassava roots
Peel/Wash
Grate
Mill
Chip and grind
Key
Dewater
Material
Bagged Mash
Mix with water 4, 6, 10 day
Unit Operation
Cake breaking
Dry
Mill finely
Ferment 2, 4 and 6 days
Dewater
Pulverise/sift
Roast Cassava flour Gari
Filter
Wash
Dewater
Dry
Mill
Starch
Fig. 1. Processing steps for the selected cassava-based products.
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dles, breakfast cereals, pies, etc. Presently in Nigeria, there is a regulation that all wheat flour should contain 10% cassava flour and this presently requires 200,000 tonnes of cassava flour in which only about 10,000 tonnes have been supplied. One of the important reasons responsible for this is the standard required to the cassava flour which is high and cannot be compromised (Oke, 2005). The estimated market value of cassava flour in Nigeria is $25million/annum with an employment potential of about 800,000 jobs at two people/tonne. To be able to satisfy this demand, about 400 small scale factories producing 2 tonnes of cassava flour per day are needed (Oke, 2005). 2. Materials and methods Processing data for the three products were gathered by direct measurement during the normal operation period of six randomly selected mills specializing in the production of each selected cassava-based food. The random selection was done from the pool of mills that were not only up to date in their data management practice but also less than 10 years old to ensure that they were within their useful years. The areas of study covered Osun and Oyo States of Nigeria. Osun and Oyo States cover an area of 17,600 sq. km and have a total population of about 5 million people (1991 census), with a population density of about 284 people per sq. km. The energy analysis was based on process analysis in which the direct energy consumption in every successive production step was estimated and the materials input to each operation also indicated. The principal operations involved in the production of each selected cassava-based food are highlighted in Fig. 1. The estimation of thermal energy (obtained from the use of fuel), electrical energy (obtained from electricity use from the national grid) and manual energy (from human labour) was done as follows (Jekayinfa & Bamgboye, 2004, 2006, 2007). 2.1. Evaluation of electrical energy The rated horsepower of each motor was multiplied by the corresponding hours of operation and summed to find the electrical energy usage by equipment. A motor efficiency of 80% was assumed to compute the electrical inputs (Johnson, 1999; Rajput, 2001). 2.2. Evaluation of thermal energy Energy from fossil fuel was assigned to each unit operation according to their level of consumption. The total quantity of energy consumed from fossil fuel was converted to common energy unit (Joule) by multiplying the quantity of fuel consumed by the corresponding calorific value (lower heating value) of the fuel used (Ezeike, 1981; Johnson, 1999; Rajput, 2001).
2.3. Evaluation of manual energy Manual energy was estimated based on the value recommended by Odigboh (1997). According to Odigboh (1997), at the maximum continuous energy consumption rate of 0.30 kW and conversion efficiency of 25%, the physical power output of a normal human labour in tropical climates is approximately 0.075 kW sustained for an 8–10 h work day. All other factors affecting manual energy expenditure were found insignificant and therefore neglected. To determine the manual energy input for a given operation, the time spent by the worker on each operation was recorded. This included the intermittent resting periods. For any unit operation, the manual energy expenditure, Em, was determined by Em ¼ 0:075 N T a
ðkWhÞ
ð1Þ
where 0.075 = the average power of a normal human labour in kW N = number of persons involved in an operation Ta = useful time spent to accomplish a given task (operation), h To access the energy demands (electrical, fuel or manual) in all the eight unit operations of cassava-based food production, quantitative data on operating conditions were measured. The energy consumed in all the unit operations involved in the production of each of the selected cassavabased products was measured and a series of equations were developed for each of the unit operations. 2.4. Experimental procedure Before the commencement of the experiments, known quantity of fuel was measured into the empty tank of the captive electricity generator in each mill. The initial reading of the electric power-reading meter installed in each section of the mill was taken at this time. After the completion of the processing of 1000 kg of raw cassava into any of the selected three products, the quantity of the fuel left in the generator’s tank and the reading of the electric meter, installed for this purpose, were taken. The differences in these readings represented the quantity of fuel used (in litres) and the electric power consumed in (kW), respectively. For each of the operations, the number of persons involved was counted and the time taken was also recorded using a stopwatch with all intermittent resting and idle period deducted. From this procedure it was possible to assign thermal, electrical, both thermal and electrical, or manual energy, as the case may be, to each unit operation. Conversion of these raw data to energy equivalent was done using the developed energy equations. The processing facilities of all the selected mills are very similar. All the mills selected were evaluated over the same period and seasons, and as a result, the error of seasonal
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slight modifications, energy and mass flow diagrams (Figs. 2–4) were constructed for a typical gari, starch and flour mills, respectively. Various models (linear, semi-log, log-linear and polynomial) were tried to develop the functional relationship between energy input and food productivity to optimize the food production system. The selection of appropriate model for a particular situation was made on the basis of R2 value. The general equation fitted for different cassava-based foods was of the following nature (Sidhu, Singh, Singh, & Ahuja, 2001): m X n X i Aij ðxj Þi ð2Þ y ¼ A0 þ
changes was eliminated. No prior experimental conditions were used as data collection in each locality was done as the mills were in operation. The apparatus used for the study include: (i) A stop watch for measuring production time. (ii) A measuring cylinder for quantifying the amount of fuel consumed during each unit operation. For consistency, the energy components were calculated on the basis of 1000 kg of raw cassava. This approach is similar to that used in previous studies by Ezeike (1981) and Jekayinfa and Bamgboye (2003, 2004, 2007). Using energy accounting symbols presented by Singh (1978) with
i¼1
j¼1
Cassava 1000
28.13
Peeling peels 9.0
120
Washing 223.38
8.25
0.59
Grating
0.94
Dewatering Water
7.50
600
Sieving
Mass flow (kg) Electrical energy (MJ)
Manual energy (MJ) 485
Fibres
22.38
Thermal energy (MJ) 10.50
15
Frying
Unit operation
1.22
Re-sieving
0.30
Garilumps
14.60
Post-grinding
Fine textured Gari 250
Fig. 2. Energy flow diagram in a typical gari processing mill.
15
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Cassava 1000
28.13
Peeling Peels 9.0
120
Washing 223.75
8.25
0.59
0.21
Chipping
80.55
2.92 Grinding
1.20
Mixing
Mass flow (kg) Electrical energy (MJ) Thermal energy (MJ)
1.20
Filtering
Manual energy (MJ) Unit operation 0.60
Starch Washing
0.94
Dewatering Water
380
Starch 500
Fig. 3. Energy flow diagram in a typical starch producing mill.
where m is equal to 1 for linear relationship and 2 for quadratic relationship; y, yield, kg/tonne of cassava; A0, intercept; n, number of independent variables; Aij, regression coefficients; and xj is the independent variable. The sensitivities of the apparatus used in the course of this study and the error analysis were calculated . 3. Results and discussion 3.1. Energy requirement for gari processing operations Average energy inputs at different stages of production of gari are presented in Table 1 and Fig. 2. From Table 1 and Fig. 2, it was observed that in all the gari processing
mills investigated; thermal energy is mostly used, followed by manual energy and electrical energy. This shows that majority of the mills depend on fuel for operations. 75.2% of the average total energy in all the gari mills was obtained from thermal source, followed by 17.8% and 7.0% obtained from manual and electrical energy sources, respectively. This evidently shows that most of the tedious operations involved in gari processing are actually carried out mechanically with over 80% of energy consumption attributed to either the use of internal combustion engine or electric motors for operating processing machines. Considering the unit operations during gari production (Fig. 2), it was observed that all the unit operations
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223
Cassava 1000
28.13
Peeling Peeks 9.0
120
Washing 223.75
8.25
0.59
Mass flow (kg)
Grating
0.90
Dewatering
Electrical energy (MJ) Thermal energy (MJ)
Water
0.60
Manual energy (MJ)
600
Cake Breaking
Unit operation 0.60
Drying 67.13 0.15
7.30
Water
80
Milling
Cassava flour 200
Fig. 4. Energy flow diagram in a typical cassava flour producing mill.
required manual energy in different quantity. Only grating utilized all the available energy sources. The average energy use for grating (232.22 MJ) was the highest accounting for Table 1 Estimates of energy input by unit operations for production of ‘‘gari” from 1000 kg of cassava tubers Unit operation
Time (h)
Energy (MJ) Electrical
Thermal
Manual
Total
Percent of total
Peeling Washing Grating Dewatering Sieving Frying Re-sieving Post grinding Total Percent of total
25.00 8.00 1.13 2.50 2.00 2.00 3.25 2.00
– – 8.25 – – – – 14.60
– – 223.38 – – 22.38 – –
28.13 9.00 0.59 0.94 7.50 10.50 1.22 0.30
28.13 9.00 232.22 0.94 7.50 32.88 1.22 14.90
8.60 2.75 71.10 0.29 2.30 10.06 0.37 4.60
45.88
22.85 7.00
245.76 75.20
58.18 17.80
326.79 100.00
100.00
71.1% of the total energy consumption. This was followed by frying (32.88 MJ, 10.06%), peeling (28.13 MJ, 8.6%) and post grinding (4.9 MJ, 4.6%). Other results include washing (9.0 MJ, 2.75%), sieving (7.5 MJ, 2.3%), re-sieving (1.22 MJ, 0.37%) and dewatering (0.94 MJ, 0.29%). In all, the total energy requirement for processing 1000 kg of cassava tuber into ‘gari’ is 326.79 MJ. The mean value of errors between the measured value and the true value was 0.152. The standard deviation of the differences in the 6 mills was 0.174 with a worst-case error of 0.03. 3.2. Energy requirement for production of cassava starch Table 2 and Fig. 3 show the average energy consumption on the basis of all unit operations involved in cassava starch production in the study area. Eight readily defined unit operations common to all starch mills visited are: cassava peeling, washing, chipping, grinding, mixing, filtering, starch washing and dewatering. From Table 2, it can be
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Table 2 Estimates of energy input by unit operations for the production of cassava starch from 1000 kg of cassava tubers
Table 3 Estimates of energy input by unit operations for the production of cassava flour from 1000 kg of cassava tubers
Unit operation
Time (h)
Energy (MJ) Thermal
Manual
Total
Unit operation
Time (h)
Energy (MJ)
Electrical
Electrical
Thermal
Manual
Total
Peeling Washing Chipping Grinding Mixing Filtering Starch washing Dewatering Total Percent of total
25.00 8.00 1.13 0.40 4.00 4.00 2.00
– – 8.25 2.92 – – –
– – 223.75 80.55 – – –
28.13 9.00 0.59 0.21 1.20 1.20 0.60
28.13 9.00 232.59 83.68 1.20 1.20 0.60
Peeling Washing Grating Dewatering Cake breaking Drying Milling Total Percent of total
25.00 8.00 1.13 0.40 2.00
– – 8.25 – –
– – 223.75 – –
28.13 9.00 0.59 0.90 0.60
28.13 9.00 232.59 0.90 0.60
8.12 2.60 67.15 0.26 0.17
4.00 1.00 41.53
– 7.30 15.55 4.49
–
0.60 0.15 39.97 11.54
0.60 74.58 346.40 100.00
0.17 21.53 100.00
2.50 47.03
– 11.17 3.13
– 304.30 85.15
0.94 41.87 11.72
0.94 357.34 100.00
Percent of total 7.87 2.52 65.09 23.42 0.34 0.34 0.17 0.26 100.00
observed that thermal energy accounted for more than 85% of the total energy consumption in all the mills. 11.72% and 3.13% of the total energy used could be attributed to manual and electrical energy sources, respectively. This again shows that the most tedious and energy consuming operations (chipping and grinding) were performed mechanically by the use of machines powered by either an electric motor or internal combustion engine. As depicted by Table 2 and Fig. 3, only two operations (chipping and grinding) combined the use of all available energy sources. Other operations that were performed entirely manually are peeling, washing, mixing, filtering, starch washing and dewatering with average energy requirements of 28.13, 9.0, 1.20, 1.20, 0.60, and 0.94 MJ, respectively. Conclusively, the average total energy requirement for producing cassava starch in all the mills investigated is 357.34 MJ per 1000 kg of cassava tubers, with the mean value of errors of 0.158 and standard deviation of 0.185. 3.3. Energy requirement for production of cassava flour Energy consumption pattern in the production of cassava flour from selected typical mills in the study area is presented in Table 3 and Fig. 4. Production of flour comprises 7 readily defined unit operations viz: Peeling, washing, grating, dewatering, cake breaking, drying, and milling. The average total energy consumption per 1000 kg of raw cassava tubers for all these 7 unit operations in the selected cassava flour mills was 346.40 MJ. The mean value of errors between the measured values and true value was 0.165. The standard deviation of the differences in the 6 mills was 0.158 with a worst-case error of 0.03. The two prominent energy intensive operations are grating and milling, accounting for 67.15% and 21.53% of the total energy, respectively. Other operations with their percent energy contributions in parenthesis are peeling (8.12%), washing (2.60%) and cake breaking (0.17%). As with other 2 cassava products, thermal energy was the mostly used energy source with average total contribution of 83.97% of the total energy consumed. This was followed
67.13 290.88 83.97
Percent of total
by manual energy and electrical energy with percent contributions of 11.54% and 4.49%, respectively. As indicated in Fig. 4, cassava conversion rate in a typical flour-producing mill is 20%. It could be observed in all the processing mills visited that thermal energy, from the use of fuel, was prominent. This is due to incessant failure in power supply from the national grid during the period this study lasted. 3.4. Optimization equations Eqs. (3)–(5) are the optimization equations developed for the selected cassava-based foods from the raw energy data collected on the basis of different unit operations. The production output of different products under study was optimized by multi-variable technique with no constraints with respect to various energy inputs. The technique enables the maximum production output achieved and the optimum value of each unit operation to be calculated for each product. Table 4 is the summary of data collected on observed and optimum values for product output and energy inputs from different unit operations. 3.4.1. Gari Both linear and quadratic relationships gave significant correlation for gari production. On the basis of R2 value of 0.85, which is the higher value, linear model was selected as given in Eq. (3) Y g ¼ 25Plg þ 8W g þ 1:3Gg þ 2:5Dg þ 20S g þ 20F g þ 3:2Rg þ 2P g
ð3Þ
Variation explained by this relationship was 85% and the remaining 15% variation might be due to unforeseen and sometimes, unexplainable parameters, such as operators’ ages and experiences, equipment types and age, management practices, maturity and cultivars of cassava tuber being processed, etc. All unit operations involved in gari production contributed significantly to energy consumption. The most significant unit-operation in the developed model was grating.
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Table 4 Comparison of observed and optimum values for product output and energy inputs from different unit operations for gari, cassava starch and cassava flour Unit operation
Peeling Washing Grating Chipping Grinding Dewatering Mixing Sieving Cake breaking Filtering Frying Drying Milling Re-sieving Starch washing Post grinding Total energy (MJ) Yield (kg) a b
Gari
Cassava starch
Cassava flour
Obs. V. (MJ)a
Opt. V. (MJ)b
Change (%)
Obs. V. (MJ)
Opt. V. (MJ)
Change (%)
Obs. V. (MJ)
Opt. V. (MJ)
Change (%)
28.13 9.00 232.22 – – 0.94 – 7.50 – – 32.88 – – 1.22 – 14.90 326.79
20.15 7.00 217.92 – – 0.74 – 7.00 – – 28.50 – – 1.02 – 11.50 293.83
28.37 22.22 6.16 – – 21.28 – 6.67 – – 13.32 – – -16.40 – 22.82 10.09
28.13 9.00 – 232.59 83.68 0.94 1.20 – – 1.20 – – – – 0.60 – 357.34
18.15 7.10 – 205.00 71.81 0.74 1.00 – – 1.00 – – – – 0.40 – 305.20
35.48 21.11 – 11.87 14.09 21.28 16.62 – – 16.67 – – – – 33.33 – 14.59
28.13 9.00 232.59 – – 0.90 – – 0.60 – – 0.60 74.58 – – – 346.40
20.13 7.10 216.90 – – 0.90 – – 0.45 – – 0.60 70.38 – – – 315
28.44 21.11 6.75 – – – – – 25.00 – – – 5.63 – – – 8.85
250.00
325.00
30.00
500
585.00
+17.00
200
230
+15.00
Obs. V. – Observed value Opt. V. – Optimized value
Gari yield was maximized for the optimum level of different energy inputs. The maximized yield value was estimated to be 325 kg of gari/tonne of cassava tuber for the total energy inputs level of 293.83 MJ. The input energy as can be seen in Table 1 and Figs. 1 and 2 consisted of peeling, washing, grating, dewatering, sieving, frying, reseiving and post grinding. The optimum level in peeling operation can be achieved by using more energy per tonne of raw cassava tuber to be peeled. This can be achieved by increasing the number of persons involved in peeling. Also, optimal levels can be achieved for washing, dewatering and frying operations by the same method of increasing the energy per materials to be processed. Grating, seiving, reseiving and post grinding are also very critical operations in gari production in respect to timeliness. Therefore to achieve optimal levels of energy input, one could make use of more efficient and tested equipment for each of the operations.
connected to unforeseen parameters such as mentioned in the preceeding subsection. All the energy inputs have significant contributions to the model. Cassava starch yield was also maximized for optimum level of various energy inputs. The maximized cassava starch output was estimated to be 585 kg/tonne of raw cassava tuber for the total energy inputs level of 305.20 MJ. The energy inputs for cassava starch production were from peeling, washing, chipping, grinding, mixing, filtering, starch washing and dewatering as presented in Table 2 and Figs. 1 and 3. The comparison of observed and optimum value for various energy inputs was also presented in Table 4.
3.4.2. Cassava starch The yield of cassava starch was best explained by linear equation having tested all other models based on the R2 value. The relationship between cassava starch yield and various energy inputs is given by Eq. (4). The R2 value for the equation was 0.88
Y f ¼ 25Plf þ 8W f þ 1:13Gf þ 0:4Df þ 2CBf þ MLf
Y s ¼ 25Pls þ 8W s þ 1:3C s þ 2:5GDs þ 20M s þ 20F s þ 3:25S s þ 2Ds
ð4Þ
As observed in the case of gari, the remaining 12% variation not explained by the relationship in Eq. (4) could be
3.4.3. Cassava flour The 84% variation in yield of cassava flour was significantly explained by a linear equation (Eq. (5)) with R2 value equal to 0.84 ð5Þ
Yield of cassava flour was also maximized with multivariable technique with no constraints. The estimated yield of cassava flour from the developed equation was 230 kg/ tonne of raw cassava tuber for 315.6 MJ of total energy input from unit operations consisting of peeling, washing, grating, dewatering, cake breaking, drying and milling. Similar to the observation in the two previously discussed products, energy consumption in manual operations such as peeling, washing, dewatering, cake breaking and drying could be optimized by carefully deciding on the number of
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persons that could be involved in these operations on the basis of available work place thereby reducing time of operation, increasing production output and reducing unit cost of production. The energy inputs for grating and milling can be optimized by using high-capacity motorized grater and attrition mill, respectively. 4. Conclusions The following conclusions can be drawn from the results of this study: The observed energy requirements per 1000 kg of fresh cassava tuber for production of gari, starch and flour are 327.17 MJ, 357.35 MJ and 345 MJ, respectively. The study identified the most energy-intensive operations in each production line and concluded from optimization results that the total minimum energy inputs required for the production of gari, cassava starch and cassava flour per tonne of fresh cassava tuber were 290.53, 305.20 and 315.60 MJ, respectively. The energy consumption for all manual operations in all selected production lines could be optimized by carefully deciding on the number of persons that could be involved in these operations on the basis of available work place thereby reducing time of operation, increasing production output and reducing unit cost of production. The energy use in mechanized operations could be optimized by using efficient and high-capacity processing machines, for example, IITA (International Institute of Tropical Agriculture) developed cassava processing equipment.
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