02602nam a2200265 a 4500001001400000003000700014005001700021008004100038020002200079039010500101040000800206090002900214100002400243245017300267260003400440300006500474502010600539520130000645650001701945650001601962952013301978952012302111999002502234999007702259vtls000045478KUKTEM20251114204424.0100430t2009    my a   f m    000 0 eng d  aTHE0004111(Local) 9a201905131407bfarhanac201111240945dFidac201107132316dVLOADc201004301542diday201004301225zida  aUMP  aTP156.F4 A37 2009 rs Bc.0 aNoor Aishah Yumasir10aRunge Kutta 4th order method and matlab in modeling of biomass growth and product formation in batch fermentation using differential equations /cNoor Aishah Bt Yumasir  aKuantan, Pahang :bUMP,c2009  axiii, 70 p. :bill. (some col.) ;c30 cm. +e1 computer disc  aProject paper (Bachelor of Chemical Engineering (Biotechnology)) -- Universiti Malaysia Pahang - 20093 aThis study is about the modeling of biomass growth and PHB production in batch fermentation by using the numerical integration Runge Kutta 4th Order Method.  The data is obtained from two sources which are from Valappil et. al, 2007[1] and data from the experiment of Hishafi, 2009 [2].  In order to simulate the process, the method of ordinary differential equation, ode45 in MATLAB software was used.  The ode45 provides an essential tool that will integrate a set of ordinary differential equations numerically.  The calculation method of ode45 uses Runge Kutta 4th Order numerical integration.  The values of the parameters of the models are determined by selecting the value that will give the least square error between the predicted model and the actual data. After the modeling process, a linear regression between the parameters of the ode(as the dependent variable) and the manipulated control variable agitation rate and initial concentration of glucose (as the independent variables) is made in order to study the effect of the manipulated variables.  From the simulation, it’s found that the model for both of biomass and PHB fit the data satisfactorily.  After the linear regression, it is found that the agitation rate gives more influence than initial concentration of glucose. 0aFermentation 0aBiopolymers  00104071a10000b10000d2019-09-04l0oCD 3936 | TP156.F4 A37 2009 rs Bc.p0000042818r2019-09-04 00:00:00t1w2019-09-04yPSM  00104071a10000b10000d2019-09-04l0oTP156.F4 A37 2009 rs Bc.p0000042817r2019-09-04 00:00:00t1w2019-09-04yPSM  aVIRTUA40c1760d1766  aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5200*6500*6501*9992