النشر العلمي

  • .Evaluation of center pivot irrigation system and wheat water productivity under New Hamdab condition, Northern Sudan

Evaluation of center pivot irrigation system and wheat water productivity under New Hamdab condition, Northern Sudan

 

Hisham M.Mohammed I, Adeeb A.Mohamed2 and Habiballa A.Mohamed I

1National Institute of Desert Studies (NIDS), University of Gezira, Wad   Medani, Sudan

2Water  Management and Irrigation Institute, University of Gezira, Wad   Medani, Sudan

ABSTRACT

         This study was conducted under center pivot irrigation systems at the farms of Authority of Merowi Dam Area for Agricultural Development in New Hamdab Scheme, Northern State, from season 2007/08 to 2009/10. The aims of this study were to evaluate the performance of center pivot irrigation system including, uniformity coefficient and distribution uniformity and water productivity. The results showed that uniformity coefficients of the center pivot irrigation system was 61% to 75% and was generally below the recommended value and that the water distribution uniformity values ranged between 44% to 71% and also below the recommended values, and that the wheat water productivity was 0.21 kg/m3. The application efficiency of all the tested center pivots was 100 %, however, the irrigation requirement was not satisfied at any point along the lateral this resulted in poor yield.

تقييم اجهزة الرى المحورى و كفاءة استخدام المحصول  لمياه الرى بمشروع الحامداب الجديدة الزراعى – الولاية الشمالية

 

 

هشام موسى محمد احمد1  ، على أديب محمد2 و حبيب الله عبد الحفيظ محمد1

 

1المعهد القومي لدراسات الصحراء -  جامعة الجزيرة، ود مدني، السودان  

2معهد ادارة المياه والرى -  جامعة الجزيرة، ود مدني، السودان

 

الخلاصة

 

اجريت هذه الدراسة بأنظمة الري المحوري التابعة لهيئة تطوير الزراعة بمنطقة سد مروى بمشروع الحامداب الجديدة الزراعي بالولاية الشمالية في الفترة من 2007م الى 2010م. بهدف تقييم أداء المحاور من حيث انتظامية وتوزيع مياه الرى وكفاءة استخدام المحصول لمياه الري.أوضحت النتائج أن قيم عوامل الانتظام تتراوح بين 61% إلى 75% وهى أدنى من القيم الموصى بها. أما عوامل التوزيع فكانت تتراوح بين 44 % إلى 71%  وهى دون المعدلات الموصى بها, وكانت كفاءة استخدام المحصول لمياه الري 0.21 كجم/م3. كفاءة الإعطاء كانت مائة بالمائة لكل الاجهزة التى تم اختبارها ولكن احتياجات الرى لم تكن كافية فى كل المحاور مما أدى إلى تدنى الإنتاجية.

 

 

published in العلوم الزراعية

  • Reduction of Textile Industrial Waste water Pollution

The purpose of this study is to evaluate the environmental performance and the impacts of the textile waste water from wet processing, and to find some approaches for pollution prevention or reduction. Taha Textile Mill at Khartoum North is taken as a case. The study includes the important parameters affecting pollution overload associated with the wet processing stages, such as, desizing, scouring, bleaching, dyeing, finishing and washing. Different industrial wastewater samples were taken at different intervals of time, and subjected to various  tests, and analytical methods. The study proved that, the extensive use of chemicals, dyestuffs and water results in generation of highly polluted water differing in magnitude and quality, such as: COD, BOD, turbidity, heavy metals, anions, alkalinity, color, pH, TDS, electrical conductivity, strong smelling, high temperature, etc. When the obtained results are compared with some international standards such as (U.S EPA), Pakistan (NEQS); it is found that, they mostly exceed these international standard limits. The study proved that, pollution can be reduced by reduction of water consumption  such as: effluent reuse, discoloration , precipitation with metal salts, carbon adsorption, oxidation, etc. The study proved that, dye bath from vat and sulfur dyes can be reused for four times; the obtained shades and color fastnesses are both good. High reduction in both color concentration of the residual wastewater and pH is obtained, which will indicate that the other pollution parameters such as COD, BOD, TDS, etc. are reduced.

  1.  

 

Current concern over environmental issues is reaching fever pitch to the extent that it affects most of the working population to some degree, even in financial and banking circles. Governments are concerned with the protection of health and safety of people from potentially dangerous products. Many countries intended to produce a new generation of consumers and employees with a high level of awareness over environmental matters. (Carr, 1995).

The environmental effects of the textile industry have increased during the last decade with the introduction of fast fashion in which the media introduces new seasonal trends for each fashion season. The textile manufacturing process is characterized by a high consumption of resources: like water, fuel, and a variety of chemicals. The main environmental problems are associated with water body pollution caused by the discharge of untreated effluents, in addition to air emission of volatile organic compounds, and excessive noise.( Indian textile journal, 2009).

Textile manufacturing is one of the largest industrial wastewater producers approximately 160 liter of water per 1/kg fabric,(Amna, 2010).

In Sudan there are so many textile factories, some are capable of producing bleached, colored, and finished products. The coloration and finishing operations involve a lot of wet processing stages. Every single operation requires chemicals, dyestuffs, and auxiliaries. Some of these chemicals may have harmful effects to the environment, consequently much wastewater and effluents will be produced. Textile industries use different fibers, materials and processes. These processes include a series of stages of dry and wet processing. Among these series wet processing have the most adverse effects to  the environment. (Mutasim , 2006). Since most of the factories have no  proper treatment plants, the wastewater may directly  be released to the environment, and this may lead to pollution overload.

 

 

 

 

In Sudan there are 45 textile factories, most of them have wet processing operations. The coloration and finishing operations involve a lot of wet processing stages. Wet processes require significant quantities of water, chemicals, auxiliaries and dyestuffs,(Abosalma, 2005).

There is  low knowledge and awareness about wastewater management  and cleaner production technology. These defects are expected to generate hygienic diseases such as cancer and  asthma.

 

  MATERIALS AND METHODS

 

     Wastewater samples were collected at different processing operations from  Taha Textile Factory (TTM) located at  Khartoum north . The samples were taken at different periods of time (five batches). The samples were tested using different instruments and machines. Analytical and spectrophotometric methods were carried out. The  results were analyzed statistically  and compared with some standard acceptable limits.

 The following Experiments and Tests were carried out:

1. Tests for pH. Digital pH meter, model Hm – 20 E, Tokyo.

2. Spectrophotometric tests. UV-Spectrophotometer, model-450, Hach  Company, Loveland

3. Determination of the total dissolved solids (TDS).

4. Determination of turbidity. Turbidity meter Hach, model –2100A, Hach Company, Loveland Co.

5. Reuse of wastewater. Automatic dyeing machine, type LA-121, Japan.

6. Measurement of color. Spectrophotometer 721 model (Fen Guang Duji    Shanghai, China)  

7. Measurement of the concentration of some heavy metals. Atomic  

    Absorption Spectrophotometer 580, Hach, Loveland Co

8.Measurement of the biochemical oxygen demand (BOD) (COD) Chemical Oxygen Demand Reactor, Reflux Apparatus, Erlenm

 Dyestuffs:

     The following dyestuffs were used for the preparation of the standard solutions used to determine the dye concentrations of wastewater samples:- Sulfur black B. R, Bezathrene olive green B (vat dye), Bemacron yellow SERD (Disperse dye), and  Direct Brown R.

 

  Chemicals: All chemicals used are  laboratory purpose reagents

 Test methods:

 Tests for pH: the samples were tested for pH in two ways, one by using litmus paper and the other by using a digital pH meter (model Hm 20E Japan; using method (J.I.S., L0886, 1978).

Spectrophotometric Tests:

The samples were tested for absorbance and transmittance using spectrophotometer (Model 721 Fen Guang Du Ji – Shanghai) (J.I.S., method L0869, 1971).

Testing method for color fastness to washing , rubbing and light:

The wastewater dyed samples were tested for color fastness to washing using a Japanese Industrial Standard (J.I.S.), method L0844, 1973.

Measurement of the Color concentration:

The test method is platinum-cobalt standard method (Hach 8025), adapted from standard methods for examination of water and wastewater (water research, 1996), using a UV-Spectrometer instrument.

Measurement of anions (suphates ,chlorides):

The instrument used was UV-Spectrometer. The collection of samples, the calibration of instrument, and the standard curve were performed according to the standard method (8051). (EPA, 1992).

 Measurement of the Concentration of Some Heavy Metals:

Metals such as chromium and copper were traced in wastewater by using atomic absorption spectrophotometer. Nitric acid digestion method (APHA, 3030E) was used. After the samples were collected according to the standard method (within two hours before the test), the instrument was calibrated using standard solutions.

Measurement of Biochemical Oxygen Demand (BOD):

The method used was "ISO 5815", (1989), using 300ml incubator bottles, and air incubator, thermostatically controlled at 20 oC. The samples were collected from the factory according to the standard method (1hour before test), and the dilutions were made using air-saturated distilled water.

Measurement of the Chemical Oxygen Demand (COD): 

            The method used was standard method (ISO 6060, 1989), (http://www.iso.org/iso/en/Catalogue Detail Page. Catalogue Detail). The samples to be tested were collected according to the standard method (1hour before test). Fabric sample; Bleached cotton fabric (100%).

 

RESULTS AND DISCUSSION

 

Results of Concentrations  and pH of wastewater samples

 

Table (1) Calculated concentrations of dye wastewater samples and pH results .

 Rang of pH

Concentration of dye g/l

Absorbance

Specification of wastewater

Sample No.

 12.3 - 13.4

0.2 -0.7

0.45 -1.52

Vat dye

4

 9.9 - 12.9

1.0 -1.25

1.0 -1.6

Sulphur dye

7

 6 - 6.5

0.4

1.25

Direct dye

8

  4.6 - 5

0.24

0.35

Disperse dye

18

 

From table 1, the highest dye concentration is achieved for sample No. 7 which is Sulphur dye wastewater, whereas the lowest one is for  sample 18 which is disperse dye.

From the 19 samples tested  only three samples are acidic,  the   rest are alkaline. On comparison of pH results with some standards, most of the samples are not within the accepted limit which is (6-9), where most of the aquatic life function best within this limit. http://www.2gharta.com/wastewater)

Results of colour fastness of the reused (vat and sulfur) dye wastewater.

  The concentrations of  waste water from  vat and sulfur dyes were  calculated  using a colorimeter. The results are shown in Table (1) . The wastewater samples were reused four times for dyeing  of bleached  cotton fabric using exhaustion methods. The  concentrations of the waste water from the last reused dye bath were also estimated using heat   colorimetry. The  dyed  cotton  samples  after each cycle were then tested  for washing , rubbing, and light fastness.

 

 

 

Assessment of  the color fastness was done using Japanese  standards ; grey scales and blue scale. The results of  color fastness are  tabulated as in table 3. Statistical analysis was carried out  for reduction in concentration , and the color fastness.

The concentration of vat dye was reduced from 0.7 g/l to  0.15 g/l. The concentration of sulfur dye was  reduced from 1.0 g/l to  0.2 g/l. Consequently  the values of  BOD, COD, anions , cations, TDS,  and turbidity of the remaining wastewater were reduced  by   50 to 75%. The color fastness of the dyed samples were almost good. From table3 it can be said that, pollution load can be reduced by reusing of wastewater. It is possible to dye samples of fabric with the same dye bath to different shades (medium and light shades). The dye bath left is of low color value which can easily be treated, decolorized, or can be renovated (i.e. new dyestuff is added) and reused for many times. Reduction in color concentration is calculated as 79% for vat dye wastewater, where as it is 80% for sulfur dyes. This indicates that significant saving in color has taken place. Water is saved by 70%, energy is also saved by 45%, this is because dyeing is carried out in hot liquor, and no need to heat the dye bath for the next reuse, and this will also save time. The fastness properties of the dyed samples are good, especially the light and the washing fastness.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table (2) Results of colour fastness  of  the  reused vat and sulfur dye bath

 

Sample No.

4

7

Specification of wastewater

Vat dye

Sulfur dye

Number of reuse cycles

4

4

Concentration before reuse

0.7 g/L

1 g/L

Concentration after the last reuse

0.15 g/L

0.2 g/L

Washing fastness

Cycle no.

good

Medium- good

1st 

4

4

2nd 

4

4

3rd 

4

3-4

4th 

3-4

3-4

Rubbing fastness

Cycle no.

good

Medium-good

1st 

4

4

2nd 

4

3-4

3rd 

4

3-4

4th 

 

3-4

3-4

Light fastness

Cycle no.

good

good

1st 

6-7

6-7

2nd 

6-7

6

3rd 

6

6

4th 

6

5-6

 

        *Wash and rubbing fastness (excellent=5, good=4, medium=3, poor =2, bad=1).

          *Light fastness (excellent=8, good to very good=6-7, fair = 4-5, poor 2-3, bad=1).

 

 

 

 Results of the mean values of different parameters  for all the  wastewater samples

 

Table (3) Mean values of the results of samples for different parameters

Mean values

4

3

2

1

Range

11

10.6

9.8

11.5

11.7

pH

5859

3871

9962

2433

7170

Electrical conductivity (SCM-1)

4105

2709

6973

1703

5036

Total dissolved solids mg/l

973

348

1201

1077

1347

Alkalinity( Carbonate mg/l)

4221

3842

4380

3883

4748

Na flame photometer PPM

1842

1842

_

_

_

Turbidity NTU

 

       The mean values  of the results of pH, electrical conductivity, total dissolved  solids, alkalinity, sodium concentration, and turbidity  were calculated for all the tested samples. The average value of  pH is 11, the accepted limits in many countries ranges from pH 6-9, where most of the aquatic organisms function best. Below or above this range a serious effect on an ecosystem by indirectly altering other aspects of water chemistry, consequently water becomes more toxic  when combined with certain metals and chemicals .

   The values of TDS could be considered as indicators to pollution overload. High concentration of TDS is due to the presence of inorganic dissolved  solids which include chlorides, nitrates, sulfates, phosphates of sodium, magnesium, calcium, and iron. High concentrations of  TDS can cause water balance problems for organisms and result in lower dissolved oxygen levels (DO).   

As Table (3) show, and by comparing the results obtained with the wastewater acceptability standards, where turbidity levels for aquatic life should not exceed 100 NTU, it was found that, the mean value of turbidity is higher than the limits which are  internationally allowable.

 

 

It is known from literature that, higher level of turbidity can affect several aquatic lives; it can result in low dissolved oxygen levels by preventing photosynthesis, by blocking sun light and raising water temperature, by absorbing more heat from the Sun. (WHO, 1993).

 

Results of COD, BOD, cations, and anions

 

Table (4) Results of COD, BOD, cations,  and anions,

Chromium mg/l

Copper

mg/l

Chloride

mg/l

Sulfate

mg/l

BOD

mg/l

COD

mg/l

Type of  waste

0.00

0.00

95.7

45.6

457

1121

Washing  

0.00

0.00

342.4

68.5

426

1470

Scouring

0.00

0.00

250.8

76.3

354

1149

Bleaching

1.23

0.43

385.4

224.9

562

2124

Dyeing, (vat)

0.39

0.38

496.2

758.7

518

1850

Dyeing, (reactive)

 

  Results of Chemical and Biological Oxygen Demands (COD and BOD) are relatively high for wastewater from dye bath due to the use of jigger machines where high dosage of dyes and auxiliaries are required to achieve the desired shade, low exhaustion%, and the higher percentage of the hydrolyzed reactive dye. The values of COD and BOD from fabric preparation are also high enough compared to the standard limit values (NEQS limits 80-150 mg/L); this can be attributed to the fact that, most of the contamination in the fabric, such as dust, coloring matters, waxes, fats, sizing materials, etc., are removed here. The high values of these parameters indicate potential depletion of dissolved oxygen in the water body, which could affect  the biological activity in water environment.  

       The highest concentration of anions (sulfates, and chlorides) is reported for reactive dyes and vat dyes waste water, this may be attributed to the addition of salts during the dye manufacture for (salting out), or during dye application as electrolytes for improvement of the dye exhaustion and leveling.

 

The sulfate ion concentration of waste water from reactive dyes is the only one which exceeds the National Environmental Quality Standards (NEQS) limit which is(600mg/L).

  Copper and chromium ions were observed in wastewater from dyeing. When comparing chromium results with the NEQS limits, it is found that, the  sample (vat dyed) have got higher results than (reactive dyed). Chromium is toxic to aquatic animals, and harmful to human above the limits specified, the toxicity is a function of temperature, pH, degree of water hardness, and chromium species. The negative health effect may be “lung cancer, kidney damage, headaches”. Usually chromium has cumulative effect, which tends to accumulate in the food chain of living organisms.

   Results of Ratios of BOD to COD

 

Table (5) Ratios of BOD to COD

Sample No.

Type of  waste

COD mg/L

BOD mg/L

  Ratio of BOD/COD

30

Washing  

1121

457

0.41

31

Scouring

1470

426

0.29

32

Bleaching

1149

354

0.31

33

Dyeing, (vat)

2124

562

0.26

34

Dyeing, (reactive)

1850

518

0.28

 

  The high values of both COD and BOD as shown in table 6. indicate high organic pollution levels and a great deal of organic materials enter the water, the environmental effect is that microorganisms are required to degrade and break down the organic substances consequently the microorganisms consume large quantities of oxygen in the process. This essentially causes suffocation of aquatic organisms that require oxygen to survive.

 

 

 

   3.6  Results of waste water mean values in comparison to standards

 

Table (6)Mean values of waste water samples compared to  NEQS and U.S. EPA standards

pollution  Parameters

Mean                         Values

NEQS      Limits

U.S. Limits          EPA

pH 

11

6-9

6-9

Electrical conductivity (Scm1-)

5859

1500

1750

Total dissolved solids (mg/L)

4105

3500

2500

Alkalinity(mg carbonate /L)

1043

208

250

Na (mg/L)

4221

147

200

Turbidity (NTU)

1842

100

100

BOD(mg/L)

463

80

30

COD(mg/L)

1542

150

200

Sulfates(mg/L)

235

600

800

Chlorides(mg/L)

314

1000

800

Chromium(mg/L)

0.32

1.0

0.10

Copper (mg/L)

0.16

1.0

0.25

Color (Pt-Co)(mg/L)

1366

150

150

 

When the mean values of wastewater characteristics as in table7 are compared with the standard limits, the following points are revealed:

  1.   pH of the samples exceeds the upper limits of both NEQS and EPA standards.
  2.   The electrical conductivity, the total dissolved solids, the alkalinity, and the sodium ion both exceed the NEQS and EPA limits.
  3.   BOD, COD in all the reported cases, exceed the NEQS and EPA limits.
  4.   Sulfates and chlorides are below the NEQS and EPA limits.
  5.   Chromium and copper ions are below the NEQS limits, although they exceed the EPA limits.
  6.    Color (mean value) exceeds the NEQS and EPA limits, although certain individual cases are well within or below the limits.

 

 

 

 

CONCLUSIONS

  1. The wet processing employs much water 180 litre/ Kg of fabric.
  2. The environmental impacts of wet processing is due to various processing stages .
  3. Some substances are said to be toxic and represent a chronic health risk.
  4. The wastewater is usually disposed hot, mostly alkaline which        contains high BOD,  COD and highly colored.
  5. Heavy metals; chromium and copper are observed.
  6. Anions, higher turbidity and electrical conductivity are reported.
  7. Wastewater from dyeing, have  the highest pH, color, COD, BOD,

    electrical   conductivity, anions, cations and of high pollution load

  1. The ratios of BOD to COD are almost not within the acceptable       standard  limits,
  2. Decolourization of wastewater is obtained through, precipitation, carbon absorption, reusing or recycling. 10.Wastewater can be reused, or renovated for the same process or others.
  3. Wastewater from dyeing (vat and sulfur) can be reused  or renovated for  dyeing up to four times.
  4. The reused dye bath gives good color fastness when it is applied on  bleached   fabric .
  5. The reused wastewater strength was greatly improved, color, COD, BOD and TDS   were reduced by 50 -75%, pH is reduced to the standard range(6-9), anions and   cations were reduced by 65%.

 

 

                                              

 

 

 

 

 

 

REFERENCES

 

Assessment of Sources of Air, Water, and Land (Geneva, Switzerland,1993). http://www. Sg.awl.environment.tex.ind.pdf.

APHA-AWWA.WPCF, 1989, Standard Methods for Examination of Water and Wastewater, (17th Edition), Washington, 1989.

Abu Sadat Muhammad Sayem, and Volker Ross Bach, (2004). Progress in Supercritical Fluid Dyeing (SFD), Dresden University of Technology, D-01662, Dresden-Germany.

ATMI’s E3 Program: Encouraging Environmental Excellence Report 1995- Cases Studies of Pollution Prevention –American. Textile Industries, USA.  

Advanced Treatment of Textile Wastewater by H2O2/UV Oxidation www.environmental-expert.com/events/r.htm.

Amna, (2010). Effect of Wet Processing Chemicals on the Human Health, Sudan University of Science and Technology.

               Cleaner Production Program (CPP), May 2008.     http://www.ptg.com.pk/.pdf Central Information, legislation, Bat             notes Reports                http//www.varam.gov.IV/EIN/Pollution/Batnoes/Etekstils.htm

Carr C. M., (1995). Chemistry of the Textile Industry. (First Edition) 1995 Chapman and Hall London.

     CP Audi, EP3. Pollution Prevention Assessment for a Textile Dyeing Facility, United States. www. EP3-Pollution Prevention Ass for Tex. Dyeing Facility. Htm.

Dr. Abdalellah M. Elhassan (2005). The International Agreement, Protocols and Conventions, University of Gezira, Medani (Elhosh). 

EPA-600/4-79-020, 1983; Standard Methods, 1992, p.3-61

          Standard Method, 1992, p.3-61

Environment Agency. Finishing and Coating Oct. 2003, UK. http://www.environment-agency.gov. UK/ netregs/ processes/ …/? Version = 1 and sectored = 27544.

Glover, Brain; Pierce, H Jeffery, (1993). A Return to Nature, Journal of the Society of Dyers and Colorists, Volume (109): Jan., 1993, P 5-9. 

 

Guidelines for Industrial Wastewater from Textile Mills and  Garment http://www.bsr.org/.CSRResources/Environment/WQG.pdf

Hickman S William, (1993). Environmental Aspects of Textile Processing, Journal of the Society of Dyers and Colorists, Volume (109): January 1993, P 10- 12. 

http://www.answer.com/topic/pollution

http//www.Waste Min.com /environment.html? 200724

ISO 6060: Water Quality Determination of Chemical Oxygen http://www.iso.org/iso/en/catalogue etailpage.CatalogueDetail?

Indian Textile Journal, November 2009. www.Indian textilejournal.com

 J. Howard, (1992). The Disposal of Textile and Paper Liquid Wastes-Ministry of Commerce, Belfast.

Japanese Standard Association, (1974), JIS LO801, Japan

Kalliala, Eija M, and Pertti Nousiainen, (1999). Environmental Profile of Cotton and Polyester-Cotton Fabrics, Autex Research Journal volume (1), Finland, Europe.

Lomas, Mike, (1993). Textile Wet Processing and the Environment, Journal of the Society of Dyers and Colorists, volume (109): Jan. 1993, 10-12.

Mark S Carlough; Warren S Perkins,(1993), Charlotte, North Carolina 28266 USA, Journal of The Society of Dyers and Colorists volume (109): February 1993.p 35-37

McPhee, J. R, (1978). The Effect of Legislation and Environmental Pressure on the Textile Industry. The textile Institute and Industry-Volume (16): Number 11. November – 1978- England.

Mutasim. A,(2006).Chemistry and Finishing Technology of Textiles, University of Gezira Printing Press, Sudan.

Provisional Decree. The Environmental (Protection) Act, (2000), Sudan.

Prof. Smith Brent, (1986). Polymer and Text. Chemistry Program, NCSU, College of Textiles, Raleigh, NC.  

Peter Wragg,(1993). Wastewater Recycling, Journal of the Society of Dyers and Colorists Volume (109): September 1993,p 29-30.

Profile of China’s Textile Industry, 2003. http://www.cestt.org.cn/English/English/online-services/ Sectors/ Textile Industry. htm.

Prof. Abosalma, Abbas, Y., (2005). Mechanism of Link Scientific Research with the Textile Industry- University of Gezira, Medani.

Smith, B. 1989. ATMI,s  Dyeing and Printing Guide

          http://www.2gharta.com/wastewater.

Subrata Das (2000).Some Issues of Ecological Hazards in Textile Industry.http://www.fibre2fashion.com.pdf United Nations Environmental Program (UNEP), (1996).

     Cleaner Production in Textile Wet Processing, Work Book Trainer.                        

World Health Organization (WHO). Environmental Technology Series.

    Holme I,‘Water repellency and waterproofing’, in Textile Finishing,        Heywood D (ed.), Bradford, Society of Dyers and Colourists, 2003, 135–213.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Key words:   Dyestuff  - Wastewater  - Pollution -  colorfastness -  Fabric

published in Gezira Journal of Engineering and Applied Sciences

  • Development of a simulation model to predict the optimum machinery requirement for cutting, loading and transporting of sugarcane

ICEAI 2013

Development of a simulation model to predict the optimum machinery requirement for cutting, loading and transporting of sugarcane

 

Abdelkarim D. Elfadil1, Mamoun I. Dawelbeit, 2 Abdalla S. Abdalla1 and Yousof E. Yousof3

 

1 Department of Agricultural Engineering, Faculty of Agricultural sciences University of Gezira Wadmedani, Sudan.

2 Federal Minstry of Agriculture and Forestry, Khartoum, Sudan.

3 Department of Agricultural Engineering, Faculty of Agriculture and Natural Resources, University of Gezira,, Abu Haraz, Sudan.

Corresponding Auther: Abdelkarim D. Elfadil, karimfadil@yahoo.com

 

ABSTRACT

 

A simulation model to predict the optimum fleet of machinery required for cutting, loading and transporting of sugarcane was developed. The input data were collected during sugar-making season. Some data were collected on daily basis such as amount of cane harvested, harvester capacity, loader capacity and truck capacity. Other data were collected on weekly bases such as waiting time in the field, loading time, waiting for loading, unloading time and the time needed to travel from field to mill and vice versa. Different simulations were run to show the effect of travel speed, distance between the field and the factory, operating hours per day on the number of machinery required. From the model the manager can determine the optimum number of machinery required based on the above data and hence reducing the cost resulting from overestimating the size of fleet required.

Keywords: simulation, sugarcane, machinery, requirement.

 

INTRODUCTION

 

     In sugar industry, as in every commercial enterprise, every stage of the operation is important. Good quality sugar can only be made if high quality cane is planted. If the plants are tended well, if they are harvested and transported carefully in time to the factory, if they are processed efficiently, and if the sugar is well packaged for marketing. If any of these steps is mishandled, the consequences can be dire for everyone who involved at each point in the process chain. Harvesting and transport have a central place, providing the crucial link between the field and mill, and a point at which miscommunication and or delays can lead to irretrievable deterioration of sucrose.

     Introduction of mechanical harvesting not only achieve improved timeliness, as there is a shortage of labor during the harvesting season, but also because of the problem of handling large labor and providing accommodation for them. As the harvested sugarcane starts decaying with any delay in the extraction of juice for producing sugar, transportation facilities from the field to mill are equally important.

     It is necessary to increase the efficiency of harvesting systems to reduce the production cost of sugar in order to compete in the world market. The manager should know the optimum number of transportation units needed for least cost harvesting system.

          Modern machines in general, if properly operated and given the necessary field maintenance will operate for long periods and do a great deal of work before major repairs are required FAO[1]. Experience has shown that even with good supervision, field maintenance is often neglected, and although competent operators may be available accidents will happen which damage equipment. Machine breakdowns can be very expensive, not only from the standpoint of the expenditure necessary for repair, but also because the disastrous effect on crop productivity and the fact that idle staff must still be paid.

     The wide variation in downtime severity and breakdown frequency from season to season, create a major management problem. One management approach is to invest heavily in machinery to combat the most adverse conditions, which results in substantial spares machines capacity under average or fewer breakdown conditions.

     Parson et al [2] reported that some farmers machinery purchasing patterns have tended toward acquiring bigger machinery capacity than can, apparently, be economically justified. Yet this investment in a type of insurance against untimely field operations has been quite profitable for many.

     It has become a complex economic problem to decide the type and numbers of different machines a factory should acquire. What kind of transport system should be used under a particular set of conditions? How many hours should these machines be used every day? Is it more economical to use one system or combination of systems?

     To avoid larger numbers of trucks found in the field waiting for loading, which will contribute to additional cost, the suggestion is that more efficient planning of the operations prior processing is needed. Transportation planning can lead to reduction of the number of trucks in the field. In addition, it will also decrease the period between cane burning and processing, and reduce the quantity of spoiled cane.

     Mechanization has been viewed as a solution to the labor problem. However complete mechanization may not be efficient for the industry. But improved equipment, better management practices and adjustments at the mill are making mechanical harvesting more advantageous over time.

     Power required for field operations depends on the amount of work to be done at the time schedule. Work to be done depends on weather conditions, soil characteristics, operations required and the technology being used.

     To avoid this larger numbers of trucks found in the field waiting for loading, which will contribute to additional cost, the suggestion is that more efficient planning of the operations prior to processing is needed. This planning can lead to the reduction of the number of trucks in the field. In addition, it will also decrease the time in which the burnt cane is left unprocessed, which reduce the quantity of spoiled cane substantially.

     Bredstrom et al. [3], Bradely and Winsauer [4], in their studies have pointed out the importance of inbound logistics systems to assure continuous and uniform feeding of sugarcane, orange and wood to the processing facilities.

      The reception operation in the processing plant yard include the processes from the initial weighing of the vehicles loaded with the raw materials to their unloading in the processor’s cranes and conveyors. Therefore, trucks, upon arriving at a processing plant, go through several operations such as net weighing on a scale, sampling tests to determine content quality, unloading on intermediary storage areas and/or on the processor’s crane and conveyors. The truck waiting times in the various queues of the reception area are of special concern because of the possible interruptions in the production process due to shortages of raw materials (since longer waiting times delay the return of the trucks to the fields, thereby reducing their availability to transport raw material to the processing plant, as well as causing machine and workers idleness in the fields).             

     According to Semenzato [5] and Naves [6] sugarcane should be milled within a certain period after it is harvested to preserve its weight, sucrose content and juice quality. Therefore, primary concerns of logistics managers are to assure a continuous and uniform feeding of raw material at the mill, maximize the unloading rates and minimize the amount of raw material waiting in the unloading lines.

     Modeling of sugarcane harvesting and transportation systems by Whitney and Cochran ([7], Cochran and Whitney [8] using queuing theory to predict delivery rates.

     Crosely [9] Developed a computer program code named PABAC the program was applied to analyze the sugarcane transport system in developing country. The system analyses comprised of conventional tractor/trailer unit with either two or four wheel drive tractors. In validating the program, predicted and actual system performance values were compared. The program could be used interactively to model a transport system and produce prediction of the performance and operating costs of vehicles used.

     Arjona et al [10] developed a simulation model of the harvesting and transportation systems of sugarcane planting in Mexico. They found that machinery was underutilized and found possible solution to the problem. The solution involved increasing efficiency of machinery use, thereby allowed a reduction in the amount of machinery without increasing the amount of sugarcane processing time.

OBJECTIVE

The objective of this paper is to develop a computer model incorporating various systems of harvesting, transporting and hauling of sugarcane to determine the optimum machinery needed for harvesting and hauling of sugarcane.

 

MODEL DEVELOPMENT

 

     The cane supply system is considered non-terminal since, during the harvest period the transport and crushing operations occur continuously 24 hours a day, seven days a week.

     The Visual Basic software program was chosen to develop the model. The simulation system is a visual modeling system which implies that the model is developed using a flow chart methodology to depict the logic of the system.

Machinery requirement:

     Optimum machinery size required for sugarcane harvesting and transportation was determined from the sugarcane harvesting program. The inputs of the program were:, expected yield., machine capacity, daily factory quota, queuing time for loading in the field, time for loading in the field ,travel time from field to the factory, queuing time for unloading in the factory, time for unloading in the factory, travel time from factory to the field.

      Field data were collected during sugar-making season. Some data were collected on daily basis such as amount of cane harvested, harvester capacity, loader capacity and truck capacity. Other data were collected on weekly bases such as waiting time in the field, loading time, waiting for loading, unloading time and the time needed to travel from field to mill and vice versa.

      Cane was cut at a constant speed and the hauling units were loaded if the loading cranes are free, and if there was enough cane for full load in the field or they queue until a loading crane and a full load were available. After loading the transportation units go to the factory, if the unloading crane is busy the transportation unit will queue up. After unloading the truck goes back to the field.

         The time span of the model is 24 hours which is the maximum time the cane can be left at the field before processing. This time is expressed in minutes (1440 minutes). Harvesting, loading and transportation should be designed to supply the daily grinding capacity of the mill, irrespective of the types of machines used. This determines the required number of machines. The machines available for harvesting the cane are harvesters, loaders, transportation units and unloading cranes. The simulation consists of the following steps:

  1. The cane is cut at a constant speed.
  2. The transportation units arrive at the field, where they are loaded if the loaders are free, otherwise they queue up until a loading crane and a full load of cane are available.
  3. The transportation units are loaded and then moved to the factory.
  4. If the unloading cranes were busy they queue up, otherwise they start unloading.
  5. After unloading the transportation units go back to the field.

Structure of the Model:

     Harvesting, loading and transportation should be designed to supply the daily grinding capacity of the mill, irrespective of the types of machines used. This determines the required number of machines units. The mathematical equations were developed, based on the hypothesis that harvesting loading and transporting capacities should match the mill capacity. The number of harvesters required to supply the mill quota is given by the equation:

              H = (k) (MQ) (MOH) (HC) (HOH)        ……………………..…………………... (1)

Where:

H      = number of harvesters required.

k       = ratio of cane to be supplied by the harvesters.

MQ   = mill quota per day.

MOH= number of hours mill operating per day.

HC   = harvester capacity (ton/h)

HOH= number of hours the harvester is in operation per day.

And the number of loaders required to load the cut cane is calculated from equation (2) as follows:

L = {(H) (HC) (HOH)} ÷ {(LC) (LOH)} ……………………….. ………….……...     (2)

Where:

L  = number of loaders.

LC = loader capacity (ton/h).

LOH = number of hours the loader is in operation per day.

The transportation units can be obtained from the formula (3) as shown:

T = {(H) (HOH) (HC) (R)} ÷ {(TC) (TOH)} …………………………………..……    (3)

Where:

T      = transportation units required.

TC   = capacity of the transportation unit (ton).

R     = time required to make one trip (hours)

TOH = number of hours the transportation unit is in operation per day.

     The time required for making one trip of the transportation unit between the field and the factory is dependent on the distance of the field and is given by the equation (4).

 

R = WL + (TC/rLC) + 2D/V +WU     ……………………….;…………….………       (4)

Where:

WL   = waiting for loading (hours).

r      = coefficient indicating the number of trailer used.

D    = distance of the harvesting site (km).

WU = waiting for unloading (hours).

V = speed of the transportation unit (km/h).

 Validation of the model:

       . The model was validated using the comprehensive data collected during the study period. Actually, output from the model was very consistent with real data. Once the model was validated, many simulation runs were performed. A sensitivity analysis was performed on a number of factors in order to determine the effect of possible variations.

Determination of machinery required

       For a given transportation time, increasing the transport units correspondingly increases the quantity of cane until the optimum factory quota is acquired. After this more numbers of units become redundant and an increase in their number does not translate into increase in the quantity of cane. The machinery required for sugarcane harvesting and hauling will be obtained from the model. Many simulation runs were carried out and it was found that there was an overinvestment in the transportation machinery to keep the factory operating. Also additional simulation runs were conducted to show the effect of distance on the number of transporting units. Results showed that the field distance and traveling time increase the number of trucks required to deliver a given factory quota. From this study it was found that as the distance of the field increased, higher number of hauling units is required to deliver the required factory quota. After the “saturation values” is reached, using more resources does not increase the quantity of cane processed.

Factor affecting combine performance:

  1. Farm size and dimensions. Short length farms increases the number of turning and hence reduce field efficiency.
  2.   The availability of hauling units, a farm would need to have enough hauling units to keep the combine operating in the field the required time to cut the daily required quota.
  3. The soil conditions.

Table (1): Effect of operating hours on harvesters and trucks required.

Operating time

 

Number of harvesters

 

Number of trucks

 

14

 

35

 

165

 

16

 

30

 

144

 

18

 

27

 

128

 

20

 

24

 

115

 

22

 

22

 

105

 

 

Table (1) shows the effect of operating time per day on the number of harvesters and trucks required to satisfy a given factory quota. Increasing the operating time per day greatly reduce the investment required for harvesters and transporting units.

 

Table (2): Effect of travel speed on trucks required and round time

Travel speed (km/h)

 

Number of harvesters

 

Round time (hours)

 

15

 

168

 

5.10

 

20

 

135

 

4.10

 

25

 

115

 

3.50

 

30

 

102

 

3.10

 

35

 

93

 

2.90

 

40

 

86

 

2.60

 

45

 

81

 

2.30

 

50

 

76

 

 

 

 

Table (2) shows the effect of travel speed on the number of trucks required. Increasing the travel seed will reduce the number of trucks needed to transport the required quota. So little investment on scrapers and motor graders will improve the road and speed could be increased and hence the round time will be reduced.

 

Table (3): Effect of field distance on trucks required and round time.

Field distance (km)

 

Number of trucks

 

Round time (hr)

 

15

 

77

 

2.30

 

20

 

90

 

2.70

 

25

 

103

 

3.10

 

30

 

115

 

3.50

 

35

 

128

 

3.90

 

40

 

142

 

4.30

 

45

 

155

 

4.70

 

50

 

168

 

5.10

 

 

Table (3) shows the effect of field distance on the number of trucks required and the round time. Greater distances necessitate greater number of transporting units. Two scenarios are available; either to harvest the distant field with the  required number of machinery required and then reduce the number of trucks gradually as the distance become near or to divide the quota between distant and near fields.

.

REFERENCES

 

[1]   FAO. 1973. Agricultural machinery workshop design and management. FAO Agricultural Development paper No.66, FAO, Rome, Italy.

 [2]  Parson, S.D., T.W., Smith and G.W. Krutz 1981. Machinery downtime costs. Transactions of the ASAE vol.24 (3), pp541-544.

 [3]   Bredstrom, D., T. J. Lundgren, M. Ronnqvist, D. Carlsson and A. Mason 2004. Supply chain optimization in the pulp mill industry—IP models, column generation and novel constraint branches. European Journal of Operation Research 156(1), 2-22.

 [4]    Bradley, D.P., S.A Winsauer 2004. Solving wood chips transport problems with computer simulation. Available from: http://www.ncrs.fs.fed.us/pubs/rp/rp_nc138.pdf.

[5]    Semenzato, R. 1995. A simulation study of sugarcane harvesting. Agricultural systems 47(427-437). Elsevier Science limited.

[6]  Naves, M.F. 2004. The relationship of orange growers and fruit juice industry: An overview of Brazil. School of economics, Business and Accounting. Available from: http://www.abecitrus_relationship_us.

 [7]   Whitney, R.W. and B.J. Cochran 1976. Predicting sugarcane delivery rates. Transactions of the American Society of Agricultural Engineers.19: 47-48.

[8]   Cochran, B.J. and R.W. Whitney 1977. A technique for designing transport system for sugarcane. In: proceedings of the XVI congress of the international society of sugarcane technologists, September1977, Sao Polo, Brazil, vol.2. Pp2068-2079.

[9]   Crossley, C.P. 1987. The application of a computer program to the analysis of sugarcane transport - case study, Journal of Agricultural Engineering Research 36, 17-30.

[10]  Arjona, E., G.; Bueno, and L. Salazar 2001. An activity simulation model for the analysis of the harvesting and transportation systems of sugarcane plantation. Computer and Electronics in Agriculture. October, 2001, vol.22 (3), p247-264(18).

 

 

published in International conference on engineering and infrmation

  • Performance of trucks working in sugarcane transportation

this study was conducted at Kenana Sugar Estate with the objective of the evaluation of failures and downtime of trucks operating in sugarcane transportation. Results showed that there was no significant difference between seasons within the same type of trucks. there was significant difference between the different types of trucks in the frequencies of failures. the transmission system and clutch have the most failures in sugarcane transportation due to the harsh conditions of opeartion. Radiator failures also were affected by the condition of operation. Accidents contributed to about 60% of the total downtime

published in International Journal of tropical Agriculture, Vol. 33,No.4

  • Evaluation of failures of tractors working in sugarcane transportation

This study was conducted in Elguneid Sugar Factory with the objective of the evaluation of the failures of tractors operating in sugarcane transportation. Results showed that there was no significant differences between seasons for the same type of tractors. there was significant differences between the different types of tractors in the frequencies of failures. the tires and gearbox were the major failures of tractors (A) while cranking and electrical failures were the major problems of tractors type  (B).
 

published in International Journal of Tropical Agriculture vol.33,No.2

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