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SANNI, Lateef Ayodele PhD., Agricultural Engineering Obafemi Awolowo University, Nigeria

CONDUCTIVE ROTARY DRYER (CRD) FOR GARI AND CASSAVA FLOUR PRODUCTION PhD Research Case Study . SANNI, Lateef Ayodele PhD., Agricultural Engineering Obafemi Awolowo University, Nigeria Supervisor: FABORODE, Michael Oladimeji INORMS 2014 ENABLING THE GLOBAL RESEARCH ENTERPRISE

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SANNI, Lateef Ayodele PhD., Agricultural Engineering Obafemi Awolowo University, Nigeria

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  1. CONDUCTIVE ROTARY DRYER (CRD) FOR GARI AND CASSAVA FLOUR PRODUCTIONPhD Research Case Study SANNI, LateefAyodele PhD., Agricultural Engineering ObafemiAwolowo University, Nigeria Supervisor: FABORODE, Michael Oladimeji INORMS 2014 ENABLING THE GLOBAL RESEARCH ENTERPRISE FROM POLICY TO PRACTICE Washington Hilton, Washington D.C., April 10-13, 2014

  2. Introduction/Justification of Research Work: Nigeria is the World’s largest producer of CASSAVA – a crop that is now a major raw material for many food, pharmaceutical and industrial products which have the potential for increasing GDP and transforming the economy. Hitherto, over 95% of Nigeria’s CASSAVA is processed into local food stuffs with little or no emphasis on industrial processing or exportation. Cassava flour and gari are two products with high demand in both local and international markets, but their industrial production is limited by lack of industrial processing equipment – most critical of which is the “DRYER”.

  3. Process of gari and cassava flour production

  4. POPULAR TRADITIONAL METHODS OF DRYING CASSAVA Open tray roasting for gari production Sun drying for cassava flour production THESE TRADITIONAL PROCESSING METHODS ARE NOT ADEQUATE FOR INDUSTRIAL/COMMERCIAL PRODUCTION AND EXPORTATION

  5. 2. Specific Objectives of Research Work • To develop a conductive rotary dryer (CRD) for wet cassava meal • To model the drying kinetics of cassava meal in the CRD • To validate the developed model • To evaluate the performance of the CRD “THE UNDERLINED PHRASES LED TO THE METHODOLOGY FOR EXECUTING THE RESEARCH WORK”

  6. DESIGN OF CRD USING AUTODESK INVENTOR

  7. CONSTRUCTION OF CRD USED FOR DRYING EXPERIMENTS

  8. PRELIMINARY FIELD EXPERIMENTS TO DETERMINE LEVEL OF PROCESS PARAMETERS OF CRD • Moisture content of wet cassava meal: 47 – 51 % wb • Bulk density of wet cassava meal: 600 – 616 kg/m3 • Coefficient of friction on stainless sheet: 0.67 – 0.71 • Temperature of cassava meal during roasting increased from 26 – 97 oC. • Average temperature of frying pan ranged between 130 – 201oC • Both bulk density and coefficient of friction increased with moisture content • Fermentation did not have effect on drying kinetics of cassava meal • Temperature of cassava meal increased rapidly within first 5 minutes from 26o C to 77.5 o C for gelatinization to occur while the remaining 15 minutes was devoted to drying.

  9. CRD-BASED EXPERIMENTS1. Effects of 7 parameters on the moisture content of cassava meal at the end of 35 minutes were determined using Taguchi method and ANOVA

  10. FACTORIAL EXPERIMENTAL DESIGN For the 37 factorial design 18 drying experiments were conducted using the Taguchi array selector, (instead of 2,187 experiments)

  11. CRITICAL DESIGN AND PROCESS PARAMETERS Application of both Taguchi and Anova helped to determine the three most critical parameters that affect performance of the conductive rotary dryer (CRD)

  12. EXPERIMENTAL REFINEMENTNow that the 7 parameters have been reduced to 3, namely:1. Drum temperature; 2. Batch quantity; 3. Vapor extraction rate, the drying kinetics of cassava meal in the CRD was investigated by varying the 3 parameters at 2 levels • Batch quantity: 5kg and 8kg • Vapour extraction rate: 0.0075and 0.03 • Drum temperature: 140o C and 200o C The factorial design required that 4 drying experiments be conducted to investigate the effects of the 3 parameters on the drying kinetics of cassava meal in the CRD. THE EMPIRICAL MODELS WERE BASED ON SIX DRYING DATA SHOWN BELOW:

  13. DRYING DATA FROM THE DRYING EXPERIMENTS

  14. MODELING THE DRYING KINETICS OF CASSAVA MEAL IN THE CRD USING SIGMAPLOT 11.0 TO FIT THE DRYING DATA WITH 7 EXISTING THIN-LAYER DRYING MODELS

  15. NON-LINEAR REGRESSIONS USING SigmaPlot11.0Logarithmic Model: MR = a exp(-kt) + c; R2 = 0.993

  16. THREE MATHEMATICAL MODELS GENERATED AS A FUNCTION OF EACH PROCESS PARAMETER

  17. LIMITATION OF THE 3 MODELS ONLY ONE OF THE THREE PARAMETERS CAN BE VARIED WHILE OTHERS ARE CONSTANT, BUT IN PRACTICE, ALL 3 PARAMETERS VARY SIMULTANEOUSLY THE CHALLENGE IS THEREFORE TO GENERATE ONE GENERIC MODEL AS A FUNCTION OF THE 3 PARAMETERS THE CURVES OF MOISTURE LOSS AGAINST DRYING TIME GENERATED FROM EACH OF THE 6 DRYING DATA SHOWED THAT A LINEARITY EXISTS BETWEEN THE COMMENCEMENT OF DRYING AND A CRITICAL TIME AT WHICH THE CASSAVA MEAL REACHES SAFE MOISTURE CONTENT OF 10% (wb) OR BELOW

  18. TYPICAL MOISTURE LOSS CURVE (GENERATED FROM DATA OF EXPERIMENT 6)

  19. Multiple Regression Equation (Linearized) Based on the Linearity assumption, the following “multiple linear regression equation” was assumed to describe the drying rate (DR): DR = a + bx + cy + dz where DR is the gradient of the linear segment of the moisture loss curve and is therefore constant with each drying data x, y, z = the parameters Td, Rv, Qb respectively a, b, c, d = model constants From multiple linear regression of DR values from each data, the following general mathematical equation was generated using SigmaPlot 11.0 DR = 0.00569 + 0.000325Td + 0.998Rv – 0.0018Qb

  20. DERIVING A GENERAL EQUATION IN TERMS OF MOISTURE RATIO (MR) AS A FUNCTION OF THE 3 PARAMETERS Since and But DR = 0.00569 + 0.000325Td + 0.998Rv – 0.0018Qb the following general equation was then derived with moisture ratio (MR) being a function of the three parameters: Average correlation coefficient R2 of 0.982 between predicted and experimental values of MR validated the model for all the 6 drying data used.

  21. Policy Implications of Research: MOTIVATING FACTORS DURING THE RESEARCH WORK: • Policy: Research is an essential part of Lecturer’s function at OAU • Mentoring: Working with more experienced researchers for support • Immediate Society: Deficiencies in the traditional method of cassava processing gave inspiration to the work • Grants: Financial support from Raw Materials Research and Development Council (RMRDC) • Partnerships/Collaborations: Membership of PHTRG and DelPHE project MAJOR CONSTRAINTS DURING THE RESEARCH WORK: • Very poor power supply • Weak industrial linkage in Nigeria • Stringent University financial control system • Poor reward system for researchers, especially early career academics • Publish or Perish syndrome in Nigerian Universities puts more emphasis on journal articles than patents • Hence, lack of support for innovation and creative works

  22. Policy Implications of Research: “THE REASERCH OUTPUT (AN INDIGENOUS TECHNOLOGY) IS NOW ADOPTED BY THE OSUN STATE GOVERNMENT TO INDUSTRIALIZE GARI PRODUCTION ” “THE TECHNOLOGY FITS INTO THE AGRO-INDUSTRIAL DEVELOPMENT AGENDA OF THE NIGERIAN GOVERNMNET” “PROMOTING INNOVATION IN RESEARCH AND LINKING RESEARCH TO SOLVING CRITICAL SOCIETAL PROBLEMS IS A SURE WAY OF BUILDING RESEARCH CAPACITY IN HIGHER EDUCATION INSTITUTIONS IN DEVELOPING ECONOMIES”

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