ABSTRACT
The poor quality of locally processed grain slurry products has been linked to the traditional sieving method used to process these grain slurries due to its non-consistency, low accuracy and precision. The introduction of sieving machines significantly reduced the users workload as well as time consumption because the machine takes over the repetition of the sieving process with high precision. To mass produce the sieving unit of a slurry food processing machine with ease to meet the budget and requirements of the processors, the use of models has been identified as the best means of achieving the aim. In order to choose the best model to represent the sieving unit of an integrated grain slurry processing machine, an exploratory performance modelling of the sieving unit of an integrated grain slurry starch producing machine helped in the decision making process of choosing parameters that majorly affect the performance of the machine in order to produce different sizes of the sieving unit of an integrated grain slurry processing machine that will operate with optimum efficiency.
TABLE OF CONTENTS
Title page i
Declaration ii
Certification iii
Dedication iv
Acknowledgements v
Table of Contents vi
List of figure vii
Abstract
CHAPTER ONE: INTRODUCTION viii
1.1 Background of Study 1
1.2 Statement of the problem 4
1.3 Aim and objective of the study 5
1.4 Significance of the study 5
1.5 Scope of the study 5
1.6 Limitations of the study 5
CHAPTER TWO: LITERATURE REVIEW
2.1 Grain Slurry Processing 7
2.2 Overview on Grain Slurry Sieving 8
2.3 Exploratory Modelling 12
2.4 Specific Energy Consumption as key Systems performance and economic viability indicator 13
2.5 Mathematical Modeling of Engineering Systems 14
2.6 Empirical model 16
2.7 Mechanistic modelling 17
CHAPTER THREE: MATERIALS AND METHODS
3.1. MACHINE DESCRIPTION AND MANUFACTURING PROCEDURE 19
3.2. Model Development Procedure 22
3.2.1. Model formulation 22
3.2.2. Assumptions for the model development 23
CHAPTER FOUR: RESULT AND DISCUSSION
4.1 Model Development Steps 24
CHAPTER FIVE: CONCLUSION
5.1 Conclusion 36
5.2 Recommendations 36
References 38
LIST OF FIGURES
Fig. 3.1: Integrated slurry food milling-sieving-dewatering machine 21
Fig. 4.1: Free body diagram of sieving Auger shaft 28
Nomenclature
A Cross sectional of belt m2
V Belt speed m/s
M Mass per unit length of Belt Kg/m
T1 Tension in tight side of belt in the mill/sieving drive, N
T2 Tension in slack slide of belt in the mill/sieving drive, N,
Tmax Maximum allowable tension of Belt N
Tc Centrifugal tension in belt N
µ Coefficient of friction of the belt and pulley
δ Maximum allowable safe stress of belt N
θ Lap angle on the smaller pulley. degrees
β Groove angle of the pulley. degrees
D1 Diameter of driving pulley. m
D2 Diameter of the driven pulley. m
C Centre Distance of the two pulleys. m
T Torque on the transmission shaft. Nm
Qt Volumetric throughput of the mixer at full load Kg/s
ρ Bulk density of the mixed feed. Kg/m3
D Auger shaft diameter m
P Power consumed by the machine. Watt
Mm Total mass of the separated chaff and slurry
Kg L Length of the Auger sieving shaft m
a Distance between the left hand bearing and the pulley in the Auger shaft. (m)
hav Average height of the materials on the auger (m)
e Effective helix angle of the auger. degrees
Øs Angle of repose of the milled food on the surface of the auger, degrees
CR Radial clearance of the auger, m
Dc Auger membrane diameter, m
Na Angular velocity of the auger shaft, rpm
Pa Auger pitch, m
Ts Auger Blade thickness, m
ω Angular velocity of the auger, rad/s
Da1 Diameter of the driving pulley, m
Da2 Diameter of the driven pulley, m
Dc1 Diameter of the barrel membrane. m
Dc2 Diameter of the cone. m
ρs Bulk density of slurry kg/m3
ρch Bulk density of chaff kg/m3
Mb Maximum bending moment Nm
Mba Maximum bending moment on the Auger shaft, Nm
Mta Maximum bending on the Auger shaft, Nm
𝛍a Coefficient of friction of the belt and mixer pulley
δa Maximum allowable safe stress of belt in the milling/sieving drive N/m2
𝜃a Lap angle of the belt and smaller pulley in the milling/sieving drive ·°
βa Groove angle of the mixer pulley
Aa Cross-sectional area of milling/sieving belt m2
Ma Mass per unit length of milling/sieving belt drive, kg/m
Mm Mass per unit length of motor/mixer belt, kg/m
Wp1 Weight of pulley on the Auger shaft. N
Ws Weight of sieved slurry. N
Wy Total weight on the sieving auger shaft N
Wb Weight of the mixer barrel. N
Wch Weight of separated chaff. N
W Weight of the chaff enough to cause the spring displacement. N
δsp Spring displacement needed the open the stopper. m
RA Reaction of bearing support A of the Sieving auger shaft. Nm
RD Reaction of bearing support D of the Sieving auger shaft. Nm Qm Volumetric throughput of the dewatering at full load. m3/s ρm Bulk density of the food slurry. Kg/m3
P Power consumed by the machine. Watt
Mm Total mass of the food slurry loaded into the drum. Kg
L Length of the drum shaft. m
a Distance between the left two bearing of the dewatering drum shaft. m
b Distance between the right hand bearing and the right end of the drum shaft. m
Ø1 Fractional ineffective discharge zone of the drum.
Øe Fractional effective submergence and drying zone of the drum.
Dd1 Diameter of the pulley on the driver motor, m
Dd2 Diameter of the driven pulley on the drum, m
N1 Angular velocity of the driver, rpm
Nd Angular velocity of the drum shaft, rpm
Dd Diameter of the drum pulley, rpm
Md Mass per unit length of Belt on drum pulley Kg/m
Ad Area of Belt on drum pulley Kg/m
,Mbm Maximum bending moment Nm
Mbd Maximum bending moment on the drum shaft, Nm
Mtm Maximum twisting moment. Nm
Mtd Maximum twisting moment on the drum shaft, Nm
Vd Volume of the rotary drum. m3
rd Radius of the rotary drum. m
hd Height of the rotary drum. m
d Coefficient of friction of the belt on the drum/motor pulley
δd Maximum allowable safe stress of belt in the drum/motor drive N/m2
d Lap angle of the belt and smaller pulley in the hammer mill/mixer drive ·°
βd Groove angle of the drum pulley. Degrees
Vds Velocity of drum at discharge. Rpm
Lds Length of discharge opening of the drum. m
Bds Width of discharge opening of the drum. m
Ads Cross sectional area of the discharge opening of the drum belt. m2
Mm Mass per unit length of motor/mixer belt, kg/m
Wp1 Weight of the pulley on the end of the drum shaft. N
RA Reaction of bearing support A of the drum shaft. N
Rc Reaction of bearing support C of the drum shaft. N
t Time taken by the machine to dewater a full load of food slurry. Sec
Tp Mass throughput of the sieved slurry and chaff kg/h
SE Specific energy consumption of the machine. J/Kg
𝜂𝑣 Volumetric efficiency
𝜂𝑚 Machine efficiency
CHAPTER ONE
INTRODUCTION
1.1 Background of Study
The traditional method of processing grain slurry is tedious and drudgery (Fayose, 2008). It involves milling the grain to paste form using a grinding stone and sieving the grain paste by hand stirring the grain paste on a chiffon cloth tied firmly over a big bowl with water being added at regular intervals to wash the starch content of the paste into the bowl leaving its chaff behind on the chiffon surface. Thereafter, the filtrate is allowed to settle before its supernatant is decanted to increase the concentration of the slurry filtrate (Nwankwojike et al., 2015).
Mechanization of the milling process has been successfully achieved with the present-day powerdriven mills, development of slurry sieving machines records limited success in Nigeria and this is why the manual technique remains the only means of sieving among slurry food producers in this country (Simolowo, 2011). Apart from tedium and drudgery, Fayose (2008) stressed that manual sieving of slurry food is time wasting and unhygienic because hand stirring of the paste (human contact with the food material) involved in this process introduces germs and impurities in the product.(Fayose, 2008), further revealed that the offensive odour from the fermented ground paste is discouraging to the producers and this made them to drift from sieving with small mesh sized chiffon to large ones in order to conserve time leading to poor products with much chaff content.
In the quest of trying to mechanize the sieving process of slurry food production, (Nwankwojike et al., 2015) developed an integrated slurry food processing machine of which the sieving process is by compression sieving using screw press.
Thus, the increasing desire to adopt a suitable grain paste sieving process by all processors in this area calls for mass production of this part of the machine with full cognizance of variations in the scales/budgets of the different slurry food processors.
There is therefore the need for the prediction of optimal levels of operational parameters of the given size of the sieving unit of the machine before its fabrication in order to save cost, energy and time. Optimal operational parameters setting are values of the parameters required to produce the sieving unit with a given capacity at maximum efficiency, minimum processing time and energy consumption.
Development of energy saving equipments is one of the major international trends for product cost reduction in industries over the decades because a machine may be very efficient in operation, but the application may not be economical if its specific energy consumption is not relatively small with respect to its throughput (Nwankwojike et al., 2012).
Thus, equipment with minimum specific energy consumption remains the outstanding targets of present-day researchers and design engineers (Marcin 2005; Li and Kara, 2011). A more realistic model (mechanistic) that exploits the relationships governing the system behavior should be developed to validate the earlier experiment conducted by running the machine.
Model development can be derived from several different perspectives which fall into two main variant approaches, empirical and mechanistic modeling (Nwankwojike et al., 2012). Empirical modeling involves determination of models from experimental observations of systems or processes resulting to data-based models while mechanistic modeling makes use of fundamental laws governing the behavior of systems or processes to build their description (Ming, 2000; Sen, 2008).
Therefore, modeling using empirical approach depends on the availability of representative data from the process for model building and validation, hence, parameters of most empirical models are just numbers encapsulating combined effects and this is why sensitivity studies cannot be performed with it, because it is often very difficult to attach physical meaning to them (Ming, 2000; Nwankwojike et al., 2012). Thus, the need for a mechanistic based model in the replication of the sieving unit of the slurry food processing machine is in accord with (Agunwamba, 2007), who also revealed that most empirical models are usually good for interpolation but fail in extrapolation (that is when they are used for prediction of events outside the measured data) because model parameters are calibrated using particular sets of data.
Mechanistic models don’t have these limitations because they are not subject to flaws in data collection and analysis. It provides more realistic predictions and more can be done with it in terms of analysis because the details contained within any mechanistic model offers opportunity to test the sensitivities of the process to meaningful entities such as specific energy requirement, activation energies, heat transfer coefficients, capacities, catalysts, poisoning, etc. (Nwankwojike et al., 2012).
Although, empirical modeling delivers some form of working model in a much shorter time and cost, some of the cost involved in mechanistic modeling is recovered in terms of increased ‘deep’ knowledge of process behavior.
In practice, empirical modeling can be expensive as well because it requires large amounts of representative data and in many instances, this can only be acquired by perturbing the process via planned experiments. The consequence of this is the question of the model type required for a specific purpose.
If it is to design control algorithms, empirical models will do but if we need a model to design a new process; or one that can be used to troubleshoot a process that is behaving poorly or a model that is capable of pointing towards fundamental improvements in process operability, a mechanistic model will be most appropriate (Ming, 2000; Sen, 2008; Agunwamba, 2007; Nwankwojike et al., 2012).
Hence production of the sieving unit of the slurry food processing machine with respect to desired capacities of the users involves variation of operational parameters of the machine; and the possible value of its specific energy consumption, efficiency and throughput is required before fabrication to ensure its economic operation, a physical law based model which accounts for all the relevant parameters of the machine will be of great interest to manufacturers in predicting these characteristic responses. Thus, the objective of this study is to develop mathematical models based on mechanistic modeling technique for predicting throughput and specific energy consumption of the sieving unit of slurry food processing machine.
1.2 Statement of the problem
The traditional method of sieving in processing slurry food was mechanized by (Nwankwojike et al., 2019), using a compression sieving process in an integrated slurry food milling, sieving and dewatering machine.
The replication of the sieving unit of the machine in different sizes that will operate optimally and encourage mass production of the slurry food without much effort and time is required. Hence, a fundamental physical law-based model (mechanistic models) of the throughput, efficiency and specific energy consumption that will account for the amount of energy consumed with respect the load or the work done of the sieving unit of an integrated grain slurry starch producing machine is needed.
1.3 Aim and objective of the study
The objective of this study is to develop mathematical models based on mechanistic modeling technique for predicting throughput, efficiency and specific energy of the sieving unit of an integrated grain slurry machine through exploratory performance modelling.
The specific objectives are;
i. Development of mechanistic model for predicting throughput, efficiency and specific energy consumption of the sieving unit of an integrated grain slurry processing machine.
ii. Experimental validation/confirmation of the developed model’s prediction accuracy.
1.4 Significance of the study
Conducting exploratory performance modeling to development of mechanistic model and its simulation is very vital as it seeks to eliminates the rigours of trial and error associated in replicating machines. The developed mechanistic model will eliminate cost ineffective and indeterminate trial and error technique, allowing for precision to make mass production of the sieving unit of a slurry food milling, sieving and dewatering machine at its optimal settings with respect to making users desire easy and affordable.
1.5 Scope of the study
The sieving unit of a slurry food milling, sieving and dewatering machine developed by (Nwankwojike et al., 2019), will be used in this study.
Mathematical relationships among interacting factors will be used in the computation and the developed model compared and validated against the existing experimental data.
1.6 Limitations of the study
This study is limited to mechanistic modelling of throughput, efficiency and specific energy of sieving unit of an integrated grain slurry processing machine.
Click “DOWNLOAD NOW” below to get the complete Projects
FOR QUICK HELP CHAT WITH US NOW!
+(234) 0814 780 1594
Login To Comment