diff options
Diffstat (limited to 'Elements_Of_Mass_Transfer_Part_1/ch6.ipynb')
| -rw-r--r-- | Elements_Of_Mass_Transfer_Part_1/ch6.ipynb | 1312 |
1 files changed, 1312 insertions, 0 deletions
diff --git a/Elements_Of_Mass_Transfer_Part_1/ch6.ipynb b/Elements_Of_Mass_Transfer_Part_1/ch6.ipynb new file mode 100644 index 00000000..ab63d6e7 --- /dev/null +++ b/Elements_Of_Mass_Transfer_Part_1/ch6.ipynb @@ -0,0 +1,1312 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Chapter 6 : Drying" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.1 " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Calculation of the solid temp\n", + "\n", + "# Variable Declaration \n", + "#air leaves the pre-heater of the dryer at 325K\n", + "\n", + "H1=.005; #humidity of incoming air per kg of dry air\n", + "T1=25.; #wet bulb temperature\n", + "#moisture is removed along constant wet bulb temp. till 60per R.H is reached\n", + "# from the chart ,humidity of ai rleaving first shelf =.016 kg water /kg dry air.\n", + "\n", + "#dry bulb temp. of exit air is at 27 degree aand is at humidity of .016 kg water/kg dry air.the air is again heated to 52 degree dry bulb temp. in 2nd heater .\n", + "\n", + "#so air leaves heater at 52 degree and humidity of .016 kg water/kg dry air. when it leaves the 2nd shelf the correspondin dry bulb temp. is 34 degree and humidity is .023 kg water/kg dry air. the air enters the 3rd shelf after preheating to 52 degree . \n", + " \n", + "#similarly fro 3rd shelf , exit air has a humidity of .028 kg water/kg dry air and adry bulb temp. is 39 degree. the air is leaving the 4rth shelf has a humidity of .016 kg water/kg dry air and adry bulb temp. of 42 degree(the figure is only indicative and doed not correspond toactual one)\n", + "\n", + "print \"\\n the solid temp. correspond to wbt and they are 23, 27,32 and 34 degree respectively\"\n", + "\n", + "#part(ii)\n", + "Ybar=.032; #kg water/kg dry air#final moist air condotions \n", + "T2=42.; #dry bulb temperature\n", + "Mair=28.84; #molecular weight of air \n", + "Mwater=18; #molecular weight of water\n", + "pt=1.013*10**5; #total pressure in pascal\n", + "Vh=8315*((1/Mair)+(Ybar/Mwater))*((T2+273)/pt);\n", + "r=300; #flow rate of moist air leaving the dryer\n", + "a=r*60/Vh; #amount of dry air leaving /hr\n", + "w=a*(Ybar-0.005); # water removed /hr\n", + "\n", + "# Result\n", + "print \"\\n the water removed /hr is :%fkg /hr\"%w\n", + "\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " the solid temp. correspond to wbt and they are 23, 27,32 and 34 degree respectively\n", + "\n", + " the water removed /hr is :515.648113kg /hr\n" + ] + } + ], + "prompt_number": 22 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.2 " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# time for drying\n", + "# Variable Declaration \n", + "\n", + "%pylab inline\n", + "#table X*100,(kgmoisture/kg dry solid) N*100 (kg moisture evaporated /hr*m**2)\n", + " \n", + "# 35 30\n", + "# 25 30\n", + "# 20 30\n", + "# 18 26.6\n", + "# 16 23.9\n", + "# 14 20.8\n", + "# 12 18\n", + "# 10 15\n", + "# 9 9.7\n", + "# 8 7\n", + "# 7 4.3\n", + "# 6.4 2.511111\n", + "\n", + "\n", + "Ls=262.5; #mass of bone dry solid ais the drying surface\n", + "A=262.5/8; #both upper surafce and lower surface are exposed\n", + "Nc=0.3; #in kg/m**2*hr\n", + "x2=.06; #moisture content on wet basis finally after drying\n", + "x1=.25; #moisture content on wet basis finally after drying\n", + "Xcr=0.20; #crtical moisture content\n", + "X1=x1/(1-x1); #moisture content on dry basis intially\n", + "X2=x2/(1-x2); #moisture content on dry basis finally after drying\n", + "Xbar=0.025; #equillibrium moisture \n", + "\n", + "# Calculation \n", + "t1=Ls/(A*Nc) *(X1-Xcr); #so for constant rate period \n", + "\n", + "#for falling rate period we find time graphically\n", + "p = [.20 ,.18, .16, .14, .12, .10, .09, .08, .07, .064];\n", + "a = [3.3, 5.56, 6.25, 7.14, 8.32, 10.00, 11.11, 12.5, 14.29, 15.625];\n", + "from matplotlib.pyplot import *\n", + "\n", + "# Result\n", + "plot(p,a);\n", + "title(\"Fig.6.18 Example2 1/N vs X for fallling rate period\");\n", + "xlabel(\"X-- Moisture content, X(kg/kg)\");\n", + "ylabel(\"Y-- 1/N, hr,m**2/kg\");\n", + "\n", + "Area=1.116; #area under the curve\n", + "t2=Area *Ls/A; #falling rate period we find time graphically\n", + "ttotal=t1+t2; #total time for drying\n", + "print \"\\n the total time for drying the wet slab on wet basis is :%f min\"%ttotal\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n", + "\n", + " the total time for drying the wet slab on wet basis is :12.483556 min\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEXCAYAAABCjVgAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text": [ + "<matplotlib.figure.Figure at 0x29ae2d0>" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.3.a " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# plots of drying rate curve\n", + "\n", + "#part(i)\n", + "#table wt of wet slab,kg -- 5.0 4.0 3.6 3.5 3.4 3.06 2.85\n", + "# drying rate,kg/m**2s-- 5.0 5.0 4.5 4.0 3.5 2.00 1.00\n", + "# X,Dry basis -- 1.0 0.6 .44 0.4 .36 .224 0.14\n", + "# equillibrium relation is given under\n", + "\n", + "# Variable Declaration \n", + "p = [1.0,0.6,.44,0.4,.36,.224, 0.14];\n", + "a = [5.0,5.0, 4.5, 4.0, 3.5, 2.00, 1.00];\n", + "\n", + "i=0; #looping for calc. of 1/N \n", + "t = [0,0,0,0,0,0,0]\n", + "\n", + "# Calculation \n", + "while(i<7): #looping begins\n", + " t[i]=1./(a[i]);\n", + " i=i+1;\n", + "\n", + "from matplotlib.pyplot import *\n", + "\n", + "# Result\n", + "plot(p,a);\n", + "title(\"Fig.6.19(a) Example3 Drying Rate curve\");\n", + "xlabel(\"X-- Moisture content, X(kg/kg) ---->\");\n", + "ylabel(\"Y-- Drying Rate, N(kg/hr.m**2 ---->\");\n", + "#xset('window',1);\n", + "plot(p,t,\"o-\");\n", + "title(\"Fig.6.19(b) Example3 1/N vs X\");\n", + "xlabel(\"X-- Moisture content, X(kg/kg) --->\");\n", + "ylabel(\"Y-- 1/N, hr,m**2/kg --->\");\n", + "show()\n", + "#from X=0.6 to 0.44 ,falling rate is non linear and from X=.44 to .14 falling rate is linear\n", + "\n", + "print \"\\n from the graph we get critical moisture content as 0.6 kg moisture/kg dry solid\"\n", + "\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEXCAYAAACu1P9TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n", + "text": [ + "<matplotlib.figure.Figure at 0x34ed890>" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " from the graph we get critical moisture content as 0.6 kg moisture/kg dry solid\n" + ] + } + ], + "prompt_number": 23 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.3.b" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# total time for drying\n", + "\n", + "# Variable Declaration \n", + "#part(ii)\n", + "w1=5.; #wet of wet solid\n", + "c1=.5/(1-.5); #moisture content per kg wet solid\n", + "w2=5.*0.5; #moisture for 5kg wet solid\n", + "w3=w1-w2; #weight of dry solid\n", + "xbar=0.05; #equillibrium moisture content\n", + "Xbar=xbar/(1-xbar); #equillibrium moisture content\n", + "Ls=2.5; #mass of bone dry solid ais the drying surface\n", + "A=5.; #both upper surafce and lower surface are exposed\n", + "Nc=0.6; #in kg/m**2*hr\n", + "#from X=0.6 to 0.44 ,falling rate is non linear and from X=.44 to .14 falling rate is linear\n", + "X2=.15/(1-.15);\n", + "Xcr=.6; #kg moisture per kg dry solid\n", + "#so we can find time fro drying from 0.6 to .44 graphically and then for X=.44 to .1765\n", + "X1=1.; #moisture content on dry basis intially\n", + "\n", + "# Calculation \n", + "t1=Ls/(A*Nc) *(X1-Xcr); #time taken for constant drying rate(fromX=1 to .6) \n", + "X1=.44; #moisture content on dry basis\n", + "import math\n", + "t2=(Ls/(A*Nc))*((Xcr-Xbar)*math.log((X1-Xbar)/(X2-Xbar)));\n", + "t3=0.0336*Ls/Nc; #fro graph we get from X=.6 to .44\n", + "ttotal=t1+t2+t3; #total time for drying the wet slab\n", + "\n", + "# Result\n", + "print \"\\n the total time for drying the wet slab to 15 percent moisture on wet basis is :%f min\"%(ttotal*60)\n", + "\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " the total time for drying the wet slab to 15 percent moisture on wet basis is :59.610778 min\n" + ] + } + ], + "prompt_number": 24 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.4" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# time for drying the sheets\n", + "\n", + "#table X-- .30 .20 .18 .15 .14 .11 .07 .05\n", + "# N-- 1.22 1.22 1.14 .90 .80 .56 .22 .05\n", + "#let Ls/A=p\n", + "# Variable Declaration \n", + "p=48.; #mass of bone dry solid ais the drying surface\n", + "v=1.5*1.5*.5; #volume of material\n", + "Nc=1.22; #in kg/m**2*hr\n", + "Xcr=0.2; #crtical moisture content\n", + "X1=0.25; #moisture content on dry basis intially\n", + "X2=0.08; #moisture content on dry basis finally after drying\n", + "Xbar=0.025; #equillibrium moisture \n", + "\n", + "#tbar=(Ls/(A*Nc))*((Xcr-Xbar)*log((Xcr-Xbar)/(X2-Xbar)));\n", + "# Calculation \n", + "t1=p/(Nc) * (X1-Xcr); #time taken for constant drying rate period \n", + "#table X-- .18 .15 .14 .11 .07 .05\n", + "# 1/N-- .8772 1.11 1.25 1.7857 4.545 20\n", + "\n", + "# equillibrium relation is given under\n", + "p = [.18,.15,.14,.11,.07,.05];\n", + "a = [.8772,1.11,1.25,1.7857,4.545,20];\n", + "from matplotlib.pyplot import *\n", + "\n", + "# Result\n", + "plot(p,a);\n", + "title(\"Fig.6.20 Example4 1/N vs X for fallling rate period\");\n", + "xlabel(\"X-- Moisture content, X(kg/kg) ---->\");\n", + "ylabel(\"Y-- 1/N, hr,m**2/kg ---->\");\n", + "\n", + "a=14*.025*1; #area under the curve\n", + "t2=a*48; #time taken for varying drying period\n", + "ttotal=t1+t2; #total time taken \n", + "print \"\\n total time for drying the material from 25 to 8 percent moisture under same drying conditions is :%f hr\"%(ttotal)\n", + "\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " total time for drying the material from 25 to 8 percent moisture under same drying conditions is :18.767213 hr\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEXCAYAAABCjVgAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", + "text": [ + "<matplotlib.figure.Figure at 0x34fa550>" + ] + } + ], + "prompt_number": 25 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.5.a" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Calculation and plot of drying rates\n", + "\n", + "#table 6.5.1\n", + "#S.NO. Time (Hr) weight of wet material(kg)\n", + "# 0.0 5.314\n", + "# 0.4 5.238\n", + "# 0.8 5.162\n", + "# 1.0 5.124\n", + "# 1.4 5.048\n", + "# 1.8 4.972\n", + "# 2.2 4.895\n", + "# 2.6 4.819\n", + "# 3.0 4.743\n", + "# 3.4 4.667\n", + "# 4.2 4.524\n", + "# 4.6 4.468\n", + "# 5.0 4.426\n", + "# 6.0 4.340\n", + "# infinite 4.120\n", + "\n", + "# Variable Declaration \n", + "w=[5.314,5.238,5.162,5.124,5.048,4.972,4.895,4.819,4.743,4.667,4.524,4.468,4.426,4.340,4.120]\n", + "t=[0.0,0.4,0.8,1.0,1.4,1.8,2.2,2.6,3.0,3.4,4.2,4.6,5.0,6.0]\n", + "#part(i)\n", + "x=4.120; #weight of the dried material\n", + "print \"\\n moisture content (dry basis) \"\n", + "i=0; \n", + "p = [] #looping starts\n", + "\n", + "# Calculation \n", + "while(i<15): #calculation of moisture content\n", + " p.append((w[i]-x)/x);\n", + " print \"\\n :%f\"%p[i]\n", + " i=i+1;\n", + "\n", + "print \"Drying rate kg/hr*m**2\"\n", + "i=1;\n", + "a = []\n", + "for i in range(15):\n", + " a.append(0)\n", + " \n", + "while(i<14):\n", + " a[i] =((p[i-1]-p[i])*4.12/(t[i]-t[i-1]))\n", + " print \" %f \"%a[i]\n", + " i=i+1;\n", + "from matplotlib.pyplot import *\n", + "a[0]=.19;\n", + "a[14]=0;\n", + "\n", + "# Result\n", + "print \"\\n\\n from the above data it is clear that critical moisture content Xcr=0.11\"\n", + "plot(p,a);\n", + "title(\"Fig.6.19(a) Example3 Drying Rate curve\");\n", + "xlabel(\"X-- Moisture content, X(kg/kg) ---->\");\n", + "ylabel(\"Y-- Drying Rate, N(kg/hr.m**2 ---->\");\n", + "show()\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " moisture content (dry basis) \n", + "\n", + " :0.289806\n", + "\n", + " :0.271359\n", + "\n", + " :0.252913\n", + "\n", + " :0.243689\n", + "\n", + " :0.225243\n", + "\n", + " :0.206796\n", + "\n", + " :0.188107\n", + "\n", + " :0.169660\n", + "\n", + " :0.151214\n", + "\n", + " :0.132767\n", + "\n", + " :0.098058\n", + "\n", + " :0.084466\n", + "\n", + " :0.074272\n", + "\n", + " :0.053398\n", + "\n", + " :0.000000\n", + "Drying rate kg/hr*m**2\n", + "\n", + "\n", + " from the above data it is clear that critical moisture content Xcr=0.11\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEXCAYAAABVr8jJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text": [ + "<matplotlib.figure.Figure at 0x34fa790>" + ] + } + ], + "prompt_number": 26 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.5.b " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# amount of air required\n", + "\n", + "# Variable Declaration \n", + "#part(ii)\n", + "w1=4.934; #weight after two hours\n", + "w0=5.314; #initial weight \n", + "w2=w0-w1; # water evaporated in 2 hrs\n", + "H1=.01; #humidty of incoming air\n", + "H2=.03; #humidity of leaving air\n", + "yout=.03;\n", + "yin=.01;\n", + "\n", + "# Calculation \n", + "Gs=w2/(yout- yin); #water carried away\n", + "\n", + " # Result\n", + "print \"\\n the amount of air required in 2hours is :%f kg\"%Gs\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " the amount of air required in 2hours is :19.000000 kg\n" + ] + } + ], + "prompt_number": 27 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.5.c " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# actual time of the falling peroid\n", + "\n", + "# Variable Declaration \n", + "#part(iii)\n", + "#let us choose the consistency of 11 and 13 readings\n", + "Xbar=0; #equillibrium moisture content\n", + "Ls=4.12; #mass of bone dry solid ais the drying surface\n", + "A=1.; #both upper surafce and lower surface are exposed\n", + "Nc=0.19; #in kg/m**2*hr\n", + "X1=.098; #moisture content on dry basis intially\n", + "Xcr=.11; #kg moisture per kg dry solid\n", + "X2=0.074; #moisture content on dry basis finally\n", + "# Calculation \n", + "import math\n", + "tfall=(Ls/(A*Nc))*((Xcr-Xbar)*math.log((X1-Xbar)/(X2-Xbar)));\n", + "\n", + "# Result\n", + "print \"\\n from this data we get time as :%f hour\"%tfall\n", + "print \"\\n the actual time is 0.8 hours\"\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " from this data we get time as :0.670026 hour\n", + "\n", + " the actual time is 0.8 hours\n" + ] + } + ], + "prompt_number": 28 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.6" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# time saved in drying\n", + "\n", + "#wooden cloth is dried from 100 to 10 and then th efinal moisture content is changed to 16 percent from 10;\n", + "# Variable Declaration \n", + "Xcr=0.55; #crtical moisture content\n", + "X1=1; #moisture content on dry basis intially\n", + "X2=.1; #moisture content on dry basis finally after drying\n", + "Xbar=.06; #equillibrium moisture \n", + "#since eqn 1 is tobe divided by eqn 2 so let the value of Ls/A*Nc be = 1 as it will be cancelled \n", + "p=1; #let Ls/A*Nc be =p\n", + "#p=poly([0],'p'); #calc. of time 1\n", + "tbar=1; #since the eqns are independent of tbar\n", + "\n", + "# Calculation \n", + "import math\n", + "t1=0.5960514 #roots(tbar- p*((X1-Xcr)+(Xcr-Xbar)*math.log((Xcr-Xbar)/(X2-Xbar)))); #------eqn1\n", + "X2bar=.16; \n", + "#p=poly([0],'p'); #calc. of time 2\n", + "t2=0.8138516 #roots(tbar- p*((X1-Xcr)+(Xcr-Xbar)*math.log((Xcr-Xbar)/(X2bar-Xbar))));#------eqn2\n", + "\n", + "#let t1/t2 be = k\n", + "k=t1/t2;\n", + "ans=1./k-1; #reduction in time for drying \n", + "\n", + "# Result\n", + "print \"\\n the reduction in time for drying is :%f percent\"%(ans*100)\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " the reduction in time for drying is :36.540506 percent\n" + ] + } + ], + "prompt_number": 29 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.7 " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# time for drying the sheets from 30 to 6 percent\n", + "\n", + "#assume rate of drying in the falling rate period is directly prporional to the free moisture content\n", + "\n", + "#Ls/ A*Nc is unknown;\n", + "# Variable Declaration \n", + "Xcr=0.14; #crtical moisture content\n", + "X1=.3/(1-.3); #moisture content on dry basis intially\n", + "X2=0.1/(1-0.1); #moisture content on dry basis finally after drying\n", + "Xbar=.04; #equillibrium moisture \n", + "tbar=5; #time needed to dry from 30 to 6 percent on bone dry basis\n", + "\n", + "#let Ls / A*Nc be = p\n", + "#p=poly([0],'p'); #calc. of Ls / A*Nc be = p value\n", + "# Calculation and Result\n", + "x= 15.495992 #roots(tbar-p * ((X1-Xcr)+(Xcr-Xbar)*log((Xcr-Xbar)/(X2-Xbar))));\n", + "print \"\\n the value of Ls/ A*Nc is :%f\"%x\n", + "import math\n", + "#new X1 AND X2 are now given as follows\n", + "X1=0.3/(1-.3); #new moisture content on dry basis intially\n", + "X2=0.064; #new moisture content on dry basis finally after drying\n", + "tbar=x * ((X1-Xcr)+(Xcr-Xbar)*math.log((Xcr-Xbar)/(X2-Xbar)));\n", + "print \"\\n the time for drying the sheets from 30 to 6 percent moisture under same drying conditions is :%f hr\"%tbar\n", + "\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " the value of Ls/ A*Nc is :15.495992\n", + "\n", + " the time for drying the sheets from 30 to 6 percent moisture under same drying conditions is :6.683159 hr\n" + ] + } + ], + "prompt_number": 30 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.8.a" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# time for drying the material\n", + "# Variable Declaration \n", + "Ls=1000.; #mass of bone dry solid ais the drying surface\n", + "A=55.; #both upper surafce and lower surface are exposed\n", + "v=.75; #velocity of air\n", + "Nc=.3*10**-3; #in kg/m**2*s\n", + "x2=.2; #moisture content on wet basis finally after drying\n", + "Xcr=0.125; #crtical moisture content\n", + "X1=0.15; #moisture content on dry basis intially\n", + "X2=0.025; #moisture content on dry basis finally after drying\n", + "Xbar=0.0; #equillibrium moisture \n", + "import math\n", + "\n", + " # Calculation \n", + "tbar=(Ls/(A*Nc))*((X1-Xcr)+(Xcr-Xbar)*math.log((Xcr-Xbar)/(X2-Xbar)));\n", + "\n", + "# Result\n", + "print \"the time for drying the sheets from .15 to .025 kg water /kg of dyr solid moisture under same drying conditions is :%f hour\"%(tbar/3600);\n", + "\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "the time for drying the sheets from .15 to .025 kg water /kg of dyr solid moisture under same drying conditions is :3.807740 hour\n" + ] + } + ], + "prompt_number": 31 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.8.b " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# time saved if air velocity is increased\n", + "\n", + "#tbar=(Ls/(A*Nc))*((Xcr-Xbar)*log((Xcr-Xbar)/(X2-Xbar)));\n", + "#part(i)\n", + "# assuming only surface evaporation and assuming air moves parellel to surface\n", + "\n", + "#Nc=G**0.71; G=V*d\n", + "#so NC = k* V**.71\n", + "# Variable Declaration \n", + "Ls=1000.; #mass of bone dry solid ais the drying surface\n", + "A=55.; #both upper surafce and lower surface are exposed\n", + "v=.75; #velocity of air\n", + "Nc=.3*10**-3; #in kg/m**2*s\n", + "x2=.2; #moisture content on wet basis finally after drying\n", + "Xcr=0.125; #crtical moisture content\n", + "X1=0.15; #moisture content on dry basis intially\n", + "X2=0.025; #moisture content on dry basis finally after drying\n", + "Xbar=0.0; #equillibrium moisture \n", + "tbar=3.8077; #time to dry material ,calculated from previous part\n", + "V1=.75; #old velocity\n", + "V2=4.; #new velocity\n", + "import math\n", + "\n", + "# Calculation \n", + "Nc2=Nc*(V2/V1)**.71; #in kg/m**2*s\n", + "t2=(Ls/(A*Nc2))*((X1-Xcr)+(Xcr-Xbar)*math.log((Xcr-Xbar)/(X2-Xbar))); #if air velocity is increased to 4\n", + "t=tbar-t2/3600; #time saved\n", + "\n", + "# Result\n", + "print \"\\n the time saved , if air velocity is increased to 4 m/s: %f\"%t\n", + "\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " the time saved , if air velocity is increased to 4 m/s: 2.647593\n" + ] + } + ], + "prompt_number": 32 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.9 " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# time for drying the fibreboard\n", + "\n", + "#determine the drying condition of sample 0.3*0.3 size.sheet lost weight at rate of 10**-4 kg/s until the moisture fell to 60 percent\n", + "# Variable Declaration \n", + "x1=.75; #moisture content on wet basis\n", + "xbar=0.1; #equilllibrium moisture on dry basis\n", + "xcr=0.6; #critical moisture content\n", + "Ls=0.90; #mass of bone dry solid ais the drying surface\n", + "A=0.3*0.3*2; #both upper surafce and lower surface are exposed\n", + "#A*Nc=10**-4;\n", + "x2=.2; #moisture content on wet basis finally after drying\n", + "\n", + "# Calculation \n", + "Xcr=0.6/0.4; #crtical moisture content\n", + "X1=3; #moisture content on dry basis intially\n", + "X2=0.25; #moisture content on dry basis finally after drying\n", + "Xbar=0.1/0.9; #equillibrium moisture \n", + "import math\n", + "tbar=Ls/(10**-4) * ((X1-Xcr)+(Xcr-Xbar)*math.log((Xcr-Xbar)/(X2-Xbar)));\n", + "\n", + "# Result \n", + "print \"\\n the time for drying the sheets from 75 to 25 percent moisture under same drying conditions is :%f hr\"%(tbar/3600);\n", + "\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " the time for drying the sheets from 75 to 25 percent moisture under same drying conditions is :11.745087 hr\n" + ] + } + ], + "prompt_number": 34 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.10 " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# time for drying the moist material\n", + "\n", + "#determine the drying condition of sample 0.3*0.3 size.sheet lost weight at rate of 10**-4 kg/s until the moisture fell to 60 percent\n", + "\n", + "#Ls/ A*Nc is unknown;\n", + "# Variable Declaration \n", + "Xcr=0.16; #crtical moisture content\n", + "X1=.33; #moisture content on dry basis intially\n", + "X2=0.09; #moisture content on dry basis finally after drying\n", + "Xbar=.05; #equillibrium moisture \n", + "tbar=7; #time needed to dry from 33 to 9 percent on bone dry basis\n", + "import math\n", + "#let Ls / A*Nc be = p\n", + "#p=poly([0],'p'); #calc. of Ls / A*Nc be = p value\n", + "# Calculation \n", + "x= 24.886579 #roots(tbar-p * ((X1-Xcr)+(Xcr-Xbar)*math.log((Xcr-Xbar)/(X2-Xbar))));\n", + "\n", + "#new X1 AND X2 are now given as follows\n", + "X1=0.37; #new moisture content on dry basis intially\n", + "X2=0.07; #new moisture content on dry basis finally after drying\n", + "tbar=x * ((X1-Xcr)+(Xcr-Xbar)*math.log((Xcr-Xbar)/(X2-Xbar)));\n", + "\n", + "# Result\n", + "print \"\\n the time for drying the sheets from 33 to 9 percent moisture under same drying conditions is :%f hr\"%tbar\n", + "\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " the time for drying the sheets from 33 to 9 percent moisture under same drying conditions is :9.892970 hr\n" + ] + } + ], + "prompt_number": 35 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.11 " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# time for drying the slab from 65 to 30 percent\n", + "\n", + "# Variable Declaration \n", + "d=.22; #density of dry pulp in g/cc;\n", + "x1=.65; #moisture content on wet basis\n", + "x2=.3; #moisture content on wet basis\n", + "Ls=2.5; #mass of bone dry solid ais the drying surface in kg\n", + "A=1.5*1.5*2; #both upper surafce and lower surface are exposed\n", + "v=1.5*1.5*.5; #volume of material\n", + "Nc=1.4; #in kg/m**2*hr\n", + "Xcr=1.67; #crtical moisture content\n", + "# Calculation \n", + "X1=x1/(1-x1); #moisture content on dry basis intially\n", + "X2=x2/(1-x2); #moisture content on dry basis finally after drying\n", + "Xbar=0.0; #equillibrium moisture \n", + "import math\n", + "#initial moisture is more than Xcr, so there is constant rate drying period and only falling rate peroid is observed\n", + "tbar=(Ls/(A*Nc))*((X1-Xcr)+(Xcr-Xbar)*math.log((Xcr-Xbar)/(X2-Xbar)));\n", + "\n", + "# Result\n", + "print \"\\n the time for drying the sheets from 65 to 30 percent moisture under same drying conditions is :%f hour\"%tbar\n", + "\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " the time for drying the sheets from 65 to 30 percent moisture under same drying conditions is :0.975613 hour\n" + ] + } + ], + "prompt_number": 36 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.12" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# time for drying the slab\n", + "# Variable Declaration \n", + "d=.22; #density of dry pulp in g/cc;\n", + "\n", + "Ls=1.125*10**-2*.22*10**3; #mass of bone dry solid ais the drying surface\n", + "A=1.5*1.5*2; #both upper surafce and lower surface are exposed\n", + "v=1.5*1.5*.5; #volume of material\n", + "Nc=1.4; #in kg/m**2*hr\n", + "x2=.2; #moisture content on wet basis finally after drying\n", + "Xcr=0.46; #crtical moisture content\n", + "X1=0.15; #moisture content on dry basis intially\n", + "X2=0.085; #moisture content on dry basis finally after drying\n", + "Xbar=0.025; #equillibrium moisture \n", + "import math\n", + "#tbar=(Ls/(A*Nc))*((Xcr-Xbar)*log((Xcr-Xbar)/(X2-Xbar)));\n", + "# but initial moisture is more than Xcr, so there is constant rate drying period and only falling rate peroid is observed\n", + "\n", + "# Calculation \n", + "tbar=(Ls/(A*Nc))*((Xcr-Xbar)*math.log((X1-Xbar)/(X2-Xbar)));\n", + "\n", + "# Result\n", + "print \"\\n the time for drying the sheets from 15 to 8.5 percent moisture under same drying conditions is :%f min\"%(tbar*60)\n", + "\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " the time for drying the sheets from 15 to 8.5 percent moisture under same drying conditions is :7.525805 min\n" + ] + } + ], + "prompt_number": 37 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.13" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# time for drying the filter cake\n", + "#Ls/ A*Nc is unknown;\n", + "\n", + "# Variable Declaration \n", + "import math\n", + "Xcr=0.14; #crtical moisture content\n", + "x1=0.3; #moisture content on wet basis\n", + "x2=0.1; #moisture content on wet basis\n", + "X1=x1/(1-x1); #moisture content on dry basis intially\n", + "X2=x2/(1-x2); #moisture content on dry basis finally after drying\n", + "Xbar=0.04; #equillibrium moisture \n", + "tbar=5; #time needed to dry from 30 to 10 percent on bone dry basis\n", + "\n", + " #let Ls / A*Nc be = p\n", + "#p=poly([0],'p'); #calc. of Ls / A*Nc be = p value\n", + "x= 15.495992 #roots(tbar-p * ((X1-Xcr)+(Xcr-Xbar)*math.log((Xcr-Xbar)/(X2-Xbar))));\n", + "\n", + "#new X1 AND X2 are now given as follows\n", + "# Calculation \n", + "x1=.3; #new moisture content on wet basis\n", + "x2=0.06; #new moisture content on wet basis\n", + "X1=x1/(1-x1); #new moisture content on dry basis intially\n", + "X2=x2/(1-x2); #new moisture content on dry basis finally after drying\n", + "tbar=x * ((X1-Xcr)+(Xcr-Xbar)*math.log((Xcr-Xbar)/(X2-Xbar)));\n", + "\n", + "# Result\n", + "print \"\\n the time for drying the sheets from 30 to 10 percent moisture under same drying conditions is :%f hr\"%tbar\n", + "\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " the time for drying the sheets from 30 to 10 percent moisture under same drying conditions is :6.694188 hr\n" + ] + } + ], + "prompt_number": 38 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.14" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# time for drying the sheets\n", + "# Variable Declaration \n", + "d=450.; #density of dry pulp in kg/m**3;\n", + "thickness=0.05; #thickness in m**2\n", + "Ls=d*thickness; #mass of bone dry solid ais the drying surface\n", + "A=1.; #area in m**2\n", + "v=1*5*10**-2; #volume of material\n", + "Nc=4.8; #in kg/m**2*hr\n", + "xcr=.2;\n", + "xbar=0.02;\n", + "x1=.45; #new moisture content on wet basis\n", + "x2=0.05; #new moisture content on wet basis\n", + "\n", + "# Calculation \n", + "X1=x1/(1-x1); #new moisture content on dry basis intially\n", + "X2=x2/(1-x2); #new moisture content on dry basis finally after drying\n", + "Xbar=xbar/(1-xbar); #crtical moisture content\n", + "Xcr=xcr/(1-xcr); #equillibrium moisture \n", + "import math\n", + "#tbar=(Ls/(A*Nc))*((Xcr-Xbar)*log((Xcr-Xbar)/(X2-Xbar)));\n", + "# but initial moisture is more than Xcr, so there is constant rate drying period and only falling rate peroid is observed\n", + "tbar=Ls/(A*Nc) * ((X1-Xcr)+(Xcr-Xbar)*math.log((Xcr-Xbar)/(X2-Xbar)));\n", + "\n", + "# Result\n", + "print \"\\n the time for drying the sheets from 45 to 5 percent moisture under same drying conditions is :%f min\"%tbar\n", + "\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " the time for drying the sheets from 45 to 5 percent moisture under same drying conditions is :4.776612 min\n" + ] + } + ], + "prompt_number": 39 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.15" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# heater load per unit mass of dry air\n", + "\n", + "# Variable Declaration \n", + "t1=20.; #ambient air temperature \n", + "t2=70.; #exhaust air temperature \n", + "r1=150.; #evaporation of water \n", + "r2=.25; #outlet solid moisture content\n", + "t3=15.; #inlet solid temperature\n", + "t4=65.; #outlet solid temperature\n", + "p=5.; #power demand in KW\n", + "h=18.; #heat loss in kj\n", + "\n", + "h1=1.; #mean specific heat of dry air in kj/kg*K\n", + "h2=1.25; #mean specific heat of dry material in kj/kg*K\n", + "h3=4.18; #mean specific heat of moisture in kj/kg*K\n", + "e=2626.; #enthalpy of saturated water vapour in kj/kg\n", + "\n", + "# Calculation \n", + " #basis is 1hr\n", + "a1=r1*h3*(t4-t3); #heat required for heating 150 kg of water from 15 to 65\n", + "a2=r1*e; #heat required for 150 kg water evaporation\n", + "a3=2000*h1*(t2-t1); #heat required for heating air from 20 to 70 \n", + "a4=r2*h3*(t4-t3); #heat required for heating moisture from 15 to 65\n", + "a5=120*h2*(t4-t3); #heat required for heating dry solid from 15 to 65\n", + "hlost=h*3600; #heat lost in kj\n", + "total=(a2+a3+a4+a5+hlost)/3600.; #total heat lost \n", + "\n", + "# Result\n", + "print \"\\n :%f kW of heat required for 2000kg/hr of dry air\"%total\n", + "ans1=a2+a1; #heat needed for evaporation \n", + "print \"\\n heat needed fro evaporation is :%f\"%(ans1/3600)\n", + "ans2=(ans1/3600.)/total; #fraction of this heat needed for evaporation\n", + "print \"\\n fraction of this heat needed for evaporation:%f\"%(ans2)\n", + " \n", + "\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " :157.292292 kW of heat required for 2000kg/hr of dry air\n", + "\n", + " heat needed fro evaporation is :118.125000\n", + "\n", + " fraction of this heat needed for evaporation:0.750990\n" + ] + } + ], + "prompt_number": 40 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.16" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# surface area of the roller\n", + "# Variable Declaration \n", + "m1=.12; #initial moisture content\n", + "dT=85.; #product of 85 degree is used in design purpose\n", + "U=1700.; #overall heat transfer coefficient\n", + "m2=.4; #final moisture content\n", + "r=20.; #production rate\n", + " #4 kg of moisture is present in 100 kg product\n", + "# Calculation \n", + "t=4*20./100; # moisture content in 20 kg moisture\n", + "w=20-t; #dry solid weight\n", + "i=w*m1/(1-m1); #initial moisture content \n", + "j=i-t #water evaporated\n", + "ds=2296.1; #latent heat for vaporisation at 85 degree in kj/kg\n", + "h=j*ds; #heat required (assuming th esolid mix. enters at 85)\n", + "#U*A*dT = j*ds\n", + "A=h/(U*dT); #surface area of the roller required to produce aproduction rate of 20 kg product per hour\n", + "\n", + "# Result \n", + "print \"\\n surface area of the roller required to produce aproduction rate of 20 kg product per hour:%f m**2\"%(A/3.600);\n", + "\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " surface area of the roller required to produce aproduction rate of 20 kg product per hour:0.008025 m**2\n" + ] + } + ], + "prompt_number": 41 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 6.17" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# time for drying the sheets\n", + "\n", + "#moisture content reduces from 25 to 2\n", + "\n", + "# Variable Declaration \n", + "r=7.5*10**-5; #constant drying rate in kg/s\n", + "A1=.3*.3**2; # area of the sppecimen\n", + "Nc=r/A1; #drying rate\n", + "# Calculation \n", + "Xcr=.15/0.85; #.15 is the critical moisture content\n", + "Xo=.25/.75; #.25 is the initial moisture content\n", + "Xfinal=.02/0.98; #.02 is the final moisture content\n", + "Xbar=0; #equillibrium moisture content\n", + "A=1.2*.6*2; #area of the new solid\n", + "Ls=28.8; #bone dry weight of new solid\n", + "v1=.3*.3*.006; #volume of the old solid;\n", + "v2=.6*1.2*.012; #volume of the new solid\n", + "w2=1.8; #weight of the old solid\n", + "w3=864*10**-5*1.8*10**-5/54; #weight of the bone dry solid\n", + "\n", + " #Nc is prporional to =(t-ts) = (G)**0.71---- whrere G is the mass flow rate\n", + "v1=3.; #old velocity\n", + "Tg=52.; #old dry bulb temperature\n", + "Tw=21.; #wet bulb temperature\n", + "H=.002; #humidity \n", + "SH=0.015; #saturated humidity \n", + "vnew=5. #new velocity\n", + "Tgnew=66.; #new DBT\n", + "Twnew=24.; #new WBT\n", + "Hnew=.004; #new humidity\n", + "SH=.020; #new satuurated humidity\n", + "import math \n", + "#hence drying rate of air under new condition\n", + "Nc=4.167*10**-4*((vnew/v1)*(273+Tg)/(273+Tgnew))**0.71 * ((.019-H)/(.015-H));#drying rate of air under new condition in kg/m**2*s \n", + "DT=Ls/(A*Nc) * ((Xo-Xcr)+(Xcr-Xbar)*math.log((Xcr-Xbar)/(Xfinal-Xbar)));\n", + "\n", + "# Result\n", + "print \"\\n the time for drying the sheets from 25 to 2 percent moisture under same drying conditions is :%f hours\"%(DT/3600)\n", + "\n", + "\n", + "#end" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + " the time for drying the sheets from 25 to 2 percent moisture under same drying conditions is :3.929236 hours\n" + ] + } + ], + "prompt_number": 42 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +}
\ No newline at end of file |