Learn Programming

Complete the following assignments with Microsoft Excel or Google Sheet (1-6) and Python for (1-17). The first 6 assignments are the same problems but completed with both a Spreadsheet program (Excel or Google Sheets) and Python. Solutions are shown for each problem but should only be used as a learning resource, not to merely complete the assignment. Join Online for homework help sessions.

Python Solutions 1-2+

Python Solution 3

Python Solution

Python Solution 2

Python Solution 1

Python Solution 3

Python Solution 2

Python Solution 1

Python Solution 4

Python Solution 3

Python Solution 2

Python Solution 1

Python Solution 3

Python Solution 1

Python Solution

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Excel and Python Assignments

Complete the following assignments with Microsoft Excel or Google Sheet (1-6) and Python for (1-17). The first 6 assignments are the same problems but completed with both a Spreadsheet program (Excel or Google Sheets) and Python. Solutions are shown for each problem but should only be used as a learning resource, not to merely complete the assignment.

Google Colab 1

Google Colab 2

Google Colab 3

Google Colab 4

Google Colab 5

Google Colab 6

Python Solution 2

Python Solutions

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Knowledge Building (:cell width=30%:)

1 (:cell:)

2 (:cell:)

3 (:cell:)

4 (:cell:)

5 (:cell:)

6 (:cell:)

7 (:cell:)

8 (:cell:)

9 (:cell:)

10 (:cell:)

11 (:cell:)

12 (:cell:)

13 (:cell:)

14 (:cell:)

15 (:cell:)

16 (:cell:)

17 (:cell:)

Python Solution 1

Python Solution 2

Python Solution 3

Python Solution 4

Google Colab 9 (:cell:)

Problem 2 Data

Python Solutions

Python Solutions

Assignment #6

VBA Macros

Excel Solution 1

Excel Solution 2

Python Solutions

Assignment #7

Google Colab 7

Conditionals

Functions

Solution 1

Solution 2

Solution 3

Soluiton 4

Assignment #8

Google Colab 8

Loops

Arrays

Python Solutions

Assignment #9

Google Colab 9(:cell:) Generate Plots

Python Solution 1

Python Solution 2

Python Solution 3

Python Solution 4

Assignment #10

Google Colab 10

Temperature Control Lab

Python Solution 1

Python Solution 2

Assignment #11

Google Colab 11

Debugging

Python Solutions 1-5

Python Solution 6

Assignment #12

Google Colab 12

File Input and Output

Python Solutions 1-2

Python Solution 3

Assignment #13

Google Colab 13

Classes (collections of values and functions)

Python Solutions 1

Python Solutions 2

Assignment #14

Google Colab 14

Solve Equations

Python Solutions 1-2

Python Solution 3

Python Solution 4

Assignment #15

Google Colab 15

Data Regression

Python Solution 1

Python Solutions 2-4

Assignment #16

Google Colab 16

Solve Differential Equations

Python Solution

Assignment #17

Google Colab 17

Symbolic Derivatives

Integrals

Python Solution

Excel Solution 2

Excel Solution 3

Python Solutions

Assignment #2

Problem 2 Data File

Generate Plots

Data Analysis

Excel Solution 1

Excel Solution 2

Excel Solution 3

Python Solutions

Assignment #3

Solve Equations

Excel Solution 1

Excel Solution 2

Excel Solution 3

Excel Solution 4

Python Solutions

(:cellnr:) Assignment #4

Excel Template

HR Data

Dynamic Data (:cell:) Data Regression (:cell:) Excel Solution 1

Excel Solution 2

Excel Solution 3

Python Solutions

(:cellnr:) Assignment #5 (:cell:) Differential Equation Solution (:cell:) Excel Solution 1

Excel Solution 2

Python Solutions

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• (:toggle hide hw1_2 button show="Solution 2 (Python)":)

(:div id=hw1_2:) (:source lang=python:)

1. constants

L = 2 # m Tplate = 343 # K v = 1.45 # m/s Twater = 294 # K mu = 9.79e-4 # Pa*s rho = 998 # kg/m^3 k = 0.601 # W/m-K cp = 4.18e3 # J/kg-K

1. derived quantities

Re = rho*L*v/mu Pr = mu*cp/k Nu = 0.332 * Pr**(1.0/3.0) * Re**(1.0/2.0) h = Nu * k / L q = h * (Tplate-Twater)

print('Rate of Heat Transfer') print(str(q)+' W/m^2') (:sourceend:) (:divend:)

Conditionals

Functions

Excel Solution 2

Excel Solution 3

Python CL1

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(:cell width=25%:) Assignment (:cell width=35%:) Knowledge Building (:cell width=30%:) Solutions

(:cellnr:) Assignment #1 (:cell:)

• Conditionals
• Functions

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• Excel Solution 2
• Excel Solution 3
• Python CL1

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• (:toggle hide hw3_4 button show="Solution 4 (Python)":)

• Solutions 1-5, Solution 6

• Solutions 1-2, Solution 3

• Solution 1, Solution 2

• Temperature Control Lab
• Solution 2

• Python Solution 1

1. plot data and prediction

1. plot data and prediction

Complete the following assignments with Microsoft Excel, Google Sheets, or another similar spreadsheet program. Microsoft Excel solutions are shown for each problem but should only be used as a learning resource, not to merely complete the assignment. Python solutions are shown as examples of programming but are not required.

from gekko import GEKKO

1. constants

y1 = 0.33 y2 = 1.0-y1 P = 120.0 # kPa

1. Antoine constants
2. Benzene

ac1 = [13.7819, 2726.81, 217.572]

1. Toluene

ac2 = [13.9320, 3056.96, 217.625]

1. gekko model

m = GEKKO() T = m.Var(value=100) x1,x2 = [m.Var(value=0.5,lb=0,ub=1) for i in range(2)]

1. vapor pressure

Psat1 = m.Intermediate(m.exp(ac1[0]-ac1[1]/(T+ac1[2]))) Psat2 = m.Intermediate(m.exp(ac2[0]-ac2[1]/(T+ac2[2])))

1. Raoult's law

m.Equation(y1*P==x1*Psat1) m.Equation(y2*P==x2*Psat2) m.Equation(x1+x2==1) m.options.IMODE=1 m.solve(disp=False) print('Gekko Solution') print('x1: ' + str(x1.value)) print('x2: ' + str(x2.value)) print('T: ' + str(T.value))

• (:toggle hide hw4_1a button show="Solution 1 (Python curve_fit)":)

(:div id=hw4_1a:)

import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy.optimize import curve_fit from sklearn.metrics import r2_score

1. import data
2. Time (sec),Heart Rate (BPM)

url = 'https://apmonitor.com/che263/uploads/Main/heart_rate.txt' x = pd.read_csv(url)

1. print first rows

print('Data') print(x.head())

1. extract vectors

t = x['Time (sec)'].values ym = x['Heart Rate (BPM)'].values

1. define function for fitting

def bpm(t,c0,c1,c2,c3):

    return c0+c1*t-c2*np.exp(-c3*t)

1. find optimal parameters

p0 = [100,0.01,100,0.01] # initial guesses c,cov = curve_fit(bpm,t,ym,p0) # fit model

1. print parameters

print('Optimal parameters') print(c)

1. calculate prediction

yp = bpm(t,c[0],c[1],c[2],c[3])

1. calculate r^2

print('R^2: ' + str(r2_score(ym,yp)))

1. plot data and prediciton

plt.figure() plt.title('Heart Rate Regression') plt.plot(t/60.0,ym,'r--',label='Measured') plt.plot(t/60.0,yp,'b-',label='Predicted') plt.ylabel('Rate (BPM)') plt.xlabel('Time (min)') plt.legend(loc='best') plt.show()

• (:toggle hide hw4_1b button show="Solution 1 (Python GEKKO)":)

(:div id=hw4_1b:)

import numpy as np import pandas as pd import matplotlib.pyplot as plt from gekko import GEKKO from sklearn.metrics import r2_score

1. import data
2. Time (sec),Heart Rate (BPM)

url = 'https://apmonitor.com/che263/uploads/Main/heart_rate.txt' x = pd.read_csv(url)

1. print first rows

print('Data') print(x.head())

1. extract vectors

t = np.array(x['Time (sec)']) ym = np.array(x['Heart Rate (BPM)'])

1. GEKKO model

m = GEKKO()

1. parameters

tm = m.Param(value=t) c0 = m.FV(value=100) c1 = m.FV(value=0.01) c2 = m.FV(value=100) c3 = m.FV(value=0.01) c0.STATUS=1 c1.STATUS=1 c2.STATUS=1 c3.STATUS=1

1. variables

bpm = m.CV(value=ym) bpm.FSTATUS=1

1. regression equation

m.Equation(bpm==c0+c1*tm-c2*m.exp(-c3*tm))

1. regression mode

m.options.IMODE = 2

1. optimize

m.solve()

1. print parameters

print('Optimal parameters') print(c0.value[0]) print(c1.value[0]) print(c2.value[0]) print(c3.value[0])

1. calculate r^2

print('R^2: ' + str(r2_score(ym,bpm)))

1. plot data and prediciton

plt.figure() plt.title('Heart Rate Regression') plt.plot(t/60.0,ym,'r--',label='Measured') plt.plot(t/60.0,bpm,'b-',label='Predicted') plt.ylabel('Rate (BPM)') plt.xlabel('Time (min)') plt.legend(loc='best') plt.show()

• (:toggle hide hw4_2 button show="Solution 2 (Python)":)

(:div id=hw4_2:)

import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy.optimize import curve_fit from sklearn.metrics import r2_score

1. import data
2. time (min),y

url = 'https://apmonitor.com/che263/uploads/Main/dynamics.txt' x = pd.read_csv(url)

1. print first rows

print('Data') print(x.head())

1. extract vectors

t = x['time (min)'].values ym = x['y'].values

1. define function for fitting

def yfcn(t,tau,theta):

    n = len(t)
    res = np.zeros(n)
    for i in range(n):
        if i>=theta:
            res[i] = 5.0*(1.0-np.exp(-(t[i]-theta)/tau))
    return res

1. find optimal parameters

c,cov = curve_fit(yfcn,t,ym)

1. print parameters

print('Optimal parameters') tau = c[0] theta = c[1] print(c)

1. calculate prediction

yp = yfcn(t,tau,theta)

1. calculate r^2

print('R^2: ', r2_score(yp,ym))

1. plot data and prediciton

plt.figure() plt.title('Step Test Regression') plt.plot(t,ym,'ro',label='Measured') plt.plot(t,yp,'b-',label='Predicted') plt.ylabel('Response') plt.xlabel('Time (min)') plt.legend(loc='best') plt.show()

• (:toggle hide hw4_3 button show="Solution 3 (Python)":)

(:div id=hw4_3:)

import numpy as np import matplotlib.pyplot as plt

1. generate 1000 random numbers
2. with Poisson distribution and lambda=1

n = 1000 lam = 1 x = np.random.poisson(lam,n)

1. count number in each bin

bins=[0,1,2,3,4,5,6] hist, _ = np.histogram(x, bins)

1. plot histogram data

plt.bar(bins[0:-1],hist,label='1000 samples') plt.xlabel('bin') plt.ylabel('count') plt.title('Poisson Distribution (lambda=1)') plt.legend(loc='best') plt.show()

• Assignment #5
  • Differential Equation Solution
  • Excel Solution 1, Solution 2
  • (:toggle hide hw5_1ab button show="Solution 1ab (Python)":)

(:div id=hw5_1ab:)

import numpy as np import matplotlib.pyplot as plt from gekko import GEKKO from scipy.integrate import odeint

1. number of time points

n = 15

1. final time

tf = 7.0

1. initial concentration

Ca0 = 5.0

1. constants

k = 1 # 1/s

1. method #1 Analytical solution
2. Ca(t) = Ca(0) * exp(-k*t)

t = np.linspace(0,tf,n) Ca_m1 = Ca0 * np.exp(-k*t)

1. method #2 Euler's method

Ca_m2 = np.empty(n) Ca_m2[0] = Ca0 # kmol/m^3 for i in range(1,n):

    dt = t[i] - t[i-1]
    Ca_m2[i] = Ca_m2[i-1] - k * Ca_m2[i-1] * dt

1. method #3: GEKKO solution
2. create new gekko model

m = GEKKO()

1. integration time points

m.time = t

1. variables

Ca = m.Var(value=Ca0)

1. differential equation

m.Equation(Ca.dt()==-k*Ca)

1. set options

m.options.IMODE = 4 # dynamic simulation m.options.NODES = 3 # collocation nodes

1. simulate ODE

m.solve()

1. method #4: ODEINT from SciPy

def dCadt(t,Ca):

    return -k * Ca

Ca_m4 = odeint(dCadt,t,Ca0)

1. plot results

plt.figure(1) plt.plot(t,Ca_m1,'r-',label='Ca (Analytical)') plt.plot(t,Ca_m2,'ko',label='Ca (Euler)') plt.plot(t,Ca,'b--',label='Ca (GEKKO)') plt.plot(t,Ca,'ys',label='Ca (ODEINT)') plt.xlabel('Time (sec)') plt.ylabel(r'$C_a (kmol/m^3)$') plt.legend(loc='best') plt.show()

• (:toggle hide hw5_1c button show="Solution 1c (Python)":)

(:div id=hw5_1c:)

import numpy as np import matplotlib.pyplot as plt from gekko import GEKKO from scipy.integrate import odeint

1. number of time points

n = 15

1. final time

tf = 7.0

1. initial concentration

Ca0 = 5.0

1. constants

k = 1 # 1/s

1. method #1 Analytical solution
2. Ca(t) = Ca(0) * exp(-k*t)

t = np.linspace(0,tf,n) Ca_m1 = Ca0 * np.exp(-k*t)

1. method #2 Euler's method

t2 = np.arange(0,tf,0.5) n = len(t2) Ca_m2 = np.empty_like(t2) Ca_m2[0] = Ca0 # kmol/m^3 for i in range(1,n):

    dt = t2[i] - t2[i-1]
    Ca_m2[i] = Ca_m2[i-1] - k * Ca_m2[i-1] * dt

t3 = np.arange(0,tf,1.5) n = len(t3) Ca_m3 = np.empty_like(t3) Ca_m3[0] = Ca0 # kmol/m^3 for i in range(1,n):

    dt = t3[i] - t3[i-1]
    Ca_m3[i] = Ca_m3[i-1] - k * Ca_m3[i-1] * dt

t4 = np.arange(0,tf,2.1) n = len(t4) Ca_m4 = np.empty_like(t4) Ca_m4[0] = Ca0 # kmol/m^3 for i in range(1,n):

    dt = t4[i] - t4[i-1]
    Ca_m4[i] = Ca_m4[i-1] - k * Ca_m4[i-1] * dt

1. plot results

plt.figure(1) plt.plot(t,Ca_m1,'r-',label='Ca (Analytical)') plt.plot(t2,Ca_m2,'k.-',label='Ca (Euler 0.5)') plt.plot(t3,Ca_m3,'bo-',label='Ca (Euler 1.5)') plt.plot(t4,Ca_m4,'y--',label='Ca (Euler 2.1)') plt.xlabel('Time (sec)') plt.ylabel(r'$C_a (kmol/m^3)$') plt.legend(loc='best') plt.show()

• (:toggle hide hw5_2 button show="Solution 2 (Python)":)

(:div id=hw5_2:)

import numpy as np import matplotlib.pyplot as plt from gekko import GEKKO

1. final time

tf = 3.0

1. constants

k1 = 1.0 # L/mol-s k2 = 1.5 # L/mol-s

1. GEKKO solution
2. create new gekko model

m = GEKKO()

1. integration time points

m.time = np.arange(0,tf+0.01,0.2)

1. variables

Ca = m.Var(value=1.0) Cb = m.Var(value=1.0) Cc = m.Var(value=0.0) Cd = m.Var(value=0.0) S = m.Var(value=1.0)

1. differential equations

m.Equation(Ca.dt()==-k1*Ca*Cb) m.Equation(Cb.dt()==-k1*Ca*Cb-k2*Cb*Cc) m.Equation(Cc.dt()== k1*Ca*Cb-k2*Cb*Cc) m.Equation(Cd.dt()== k2*Cb*Cc) m.Equation(S==Cc/(Cc+Cd))

1. set options

m.options.IMODE = 4 # dynamic simulation m.options.NODES = 3 # collocation nodes

1. simulate ODE

m.solve()

1. plot results

plt.figure(1) plt.subplot(2,1,1) plt.plot(m.time,Ca,'r-',label='Ca',linewidth=2.0) plt.plot(m.time,Cb,'k.-',label='Cb',linewidth=2.0) plt.plot(m.time,Cc,'b--',label='Cc',linewidth=2.0) plt.plot(m.time,Cd,'y:',label='Cd',linewidth=3.0) plt.ylabel('Conc (mol/L)') plt.legend(loc='best')

plt.subplot(2,1,2) plt.plot(m.time,S,'k-',label='Selectivity',linewidth=2.0) plt.xlabel('Time (sec)') plt.legend(loc='best') plt.show() (:sourceend:) (:divend:)

• Assignment #6
  • VBA Macros
  • Excel Solution 1, Solution 2
  • (:toggle hide hw6_1 button show="Solution 1 (Python)":)

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• (:toggle hide hw6_2 button show="Solution 2 (Python)":)

(:div id=hw6_2:) (:source lang=python:) import numpy as np

r = 5 # m h = 10 # m F = 15 # m^3/min t = 180 # min

V_tank = np.pi * r**2 * h # m^3 V_crude_oil = F * t # m^3

if V_crude_oil > V_tank:

    print('Tank Overfilled by '           + str(V_crude_oil-V_tank) + ' m^3')

else:

    print('Not Overfilled') 

import numpy as np from scipy.optimize import fsolve

1. constants

TC = 77 # degC P = 1.0 # bar a = 2.877e8 # cm^6 bar K^0.5 / mol^2 b = 60.211 # cm^3 / mol Rg = 83.144598 # cm^3 bar / K-mol

1. derived quantities

TK = TC+273.15 # K

1. method #1: NumPy

def f(V):

    return P - Rg*TK/(V-b)+a/(np.sqrt(TK)*V*(V+b))

V_liq = fsolve(f,82) # Liquid root V_vap = fsolve(f,28600) # Vapor root print('NumPy Solution') print(V_liq,V_vap)

1. method #2: Gekko

from gekko import GEKKO m = GEKKO() V = m.Var(value=[82,28600]) m.Equation(P==Rg*TK/(V-b)-a/(m.sqrt(TK)*V*(V+b))) m.options.IMODE=2 m.solve(disp=False) print('Gekko Solution') print(V.value)

Complete the following assignments with Microsoft Excel, Google Sheets, or another similar spreadsheet program. Microsoft Excel solutions are shown for each problem but should only be used as a learning resource, not to merely complete the assignment. Python solutions are shown as a reference and introduction to programming but are not required.

• Excel Solution 2, Solution 3

plt.contourf(Vm,Tm,Pm,cmap='RdBu_r')

CS2 = plt.contour(Vm,Tm,Pm,[1.0,2.5],colors='k')

plt.xlabel('Volume (L)') plt.ylabel('Temperature (K)')

• Excel Solution 1, Solution 2, Solution 3

import pandas as pd import matplotlib.pyplot as plt import numpy as np

1. import April 2018 data or get new data from finance.yahoo.com

appl = pd.read_csv('https://apmonitor.com/che263/uploads/Main/AAPL.csv') goog = pd.read_csv('https://apmonitor.com/che263/uploads/Main/GOOG.csv')

1. xom = pd.read_csv('https://apmonitor.com/che263/uploads/Main/XOM.csv')
2. create dictionary of stocks

s = dict([('Apple',appl),('Google',goog)]) #,('ExxonMobil',xom)])

1. print column headers and starting rows (5 is default)

print('Apple Data') print(s['Apple'].head())

1. print column headers and ending close price (4 rows)

print('Google Data') print(s['Google']['Close'].tail(4))

1. basic data statistics

for i in s:

    print('Stock: ' + i)
    print(' max   : ' + str(max(s[i]['Close'])))
    print(' min   : ' + str(min(s[i]['Close'])))
    print(' stdev : ' + str(np.std(s[i]['Close'])))
    print(' avg   : ' + str(np.mean(s[i]['Close'])))
    print(' median: ' + str(np.median(s[i]['Close'])))

1. plot data

plt.figure() sty = dict([('Apple','r--'),('Google','b:'),('ExxonMobil','k-')]) ni = 0 for i in s:

    mc = max(s[i]['Close'])
    plt.plot(s[i]['Date'],s[i]['Close']/mc,sty[i],linewidth=3,label=i)
    plt.plot(s[i]['Date'],s[i]['High']/mc,sty[i],linewidth=1)
    plt.plot(s[i]['Date'],s[i]['Low']/mc,sty[i],linewidth=1)

plt.xticks(rotation=90) plt.legend(loc='best') plt.show()

import pandas as pd import matplotlib.pyplot as plt

1. import data
2. Time (sec), Heater 1, Heater 2, Temperature 1, Temperature 2

x = pd.read_csv('https://apmonitor.com/che263/uploads/Main/tclab.txt')

1. print column headers and starting 10 rows (5 is default)

print('Data') print(x.head(10))

1. plot data

plt.figure() plt.subplot(2,1,1) plt.title('Temperature Control Lab') plt.plot(x['Time (sec)'],x['Temperature 1'],'r--') plt.plot(x['Time (sec)'],x['Temperature 2'],'b-') plt.ylabel('Temp (degC)') plt.legend(loc='best')

plt.subplot(2,1,2) plt.plot(x['Time (sec)'],x['Heater 1'],'r--') plt.plot(x['Time (sec)'],x['Heater 2'],'b-') plt.ylabel('Heater (%)') plt.xlabel('Time (sec)') plt.legend(loc='best') plt.show()

import math as m

1. part a

help(m.cos) y = m.cos(0.5) print('cos(0.5): ' + str(y))

1. part b

y = m.sin(30.0*(m.pi/180.0)) print('sin(30 deg): ' + str(y))

1. part c

y = m.tan(m.pi/2.0) print('tan(pi/x): ' + str(y))

1. part d

x = 5.0 y = max(2.0*m.sqrt(x),(x**2)/2.0, (x**3)/3.0,(x**2+x**3)/5.0) print('max value: ' + str(y))

1. part e

x = 25 y = m.factorial(x) print('25!: ' + str(y))

1. part f

x = 0.5

1. if..else statement

if x<1.0:

    y = x**2

else:

    y = m.sin(m.pi*x/2.0)

1. same but one line

y = x**2 if x<1.0 else m.sin(m.pi*x/2.0) print('if statement result: ' + str(y))

1. part g

x = 4.999 y = m.floor(x) print('floor(4.999): ' + str(y))

• Excel Solution 1, Solution 2, Solution 3, Solution 4
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1. method #1: NumPy

from numpy.linalg import solve A = b = [45.0, 30.0, 15.0, 20.0, 92.0] x = solve(A,b) print('NumPy Solution') print(x)

1. method #2: Gekko

from gekko import GEKKO m = GEKKO() x1,x2,x3,x4,x5 = [m.Var() for i in range(5)] m.Equation(11*x1+3*x2+x4+2*x5==45) m.Equation(4*x2+2*x3+x5==30) m.Equation(3*x1+2*x2+7*x3+x4==15) m.Equation(4*x1+4*x3+10*x4+x5==20) m.Equation(2*x1+5*x2+x3+3*x4+14*x5==92) m.solve(disp=False) print('Gekko Solution') print(x1.value) print(x2.value) print(x3.value) print(x4.value) print(x5.value) (:sourceend:) (:divend:)

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1. method #1: NumPy

from scipy.optimize import fsolve def f(z):

    x,y=z
    f1 = 2*x**2+y**2-1
    f2 = (0.5*x-0.5)**2+2.0*(y-0.25)**2-1
    return [f1,f2]

x,y = fsolve(f,[1,1]) print('NumPy Solution') print(x,y)

1. method #2: Gekko

from gekko import GEKKO m = GEKKO() x,y = [m.Var(value=1) for i in range(2)] m.Equation(2*x**2+y**2==1) m.Equation((0.5*x-0.5)**2+2.0*(y-0.25)**2==1) m.solve(disp=False) print('Gekko Solution') print(x.value) print(y.value) (:sourceend:) (:divend:)

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• Excel Solution 1, Solution 2, Solution 3
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• Excel Solution 1, Solution 2
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• Excel Solution 1, Solution 2
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Complete the following assignments with Microsoft Excel, Google Sheets, or another equivalent spreadsheet program. Python solutions are also shown as a reference and introduction but not required.

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• (:toggle hide hw1_3 button show="Solution 3 (Python)":)

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(:source lang=python:) import numpy as np import matplotlib.pyplot as plt

1. calculate initial moles of N2

T = 298.15 # K P = 1.25 # atm V = 4000 # L Rg = 0.0821 # L*atm/mol/K n_N2 = P*V/(Rg*T) # moles of N2 n_N2 = n_N2 * 0.25 # moles with 25% remaining

1. calculate N2 pressure at different V and T

n = 20 # grid points V = np.linspace(500,1000,n) T = np.linspace(100,600,n)

1. create meshgrid

Vm,Tm = np.meshgrid(V,T)

1. calculate pressure

Pm = n_N2 * Rg * Tm / Vm

1. plot results

plt.figure() plt.contourf(Tm,Vm,Pm,cmap='RdBu_r') plt.colorbar() CS2 = plt.contour(Tm,Vm,Pm,[1.0,2.5],colors='k') plt.clabel(CS2, inline=1, fontsize=10) plt.xlabel('Temperature (K)') plt.ylabel('Volume (L)') plt.show() (:sourceend:) (:divend:)

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1. constants

L = 2 # m Tplate = 343 # K v = 1.45 # m/s Twater = 294 # K mu = 9.79e-4 # Pa*s rho = 998 # kg/m^3 k = 0.601 # W/m-K cp = 4.18e3 # J/kg-K

1. derived quantities

Re = rho*L*v/mu Pr = mu*cp/k Nu = 0.332 * Pr**(1.0/3.0) * Re**(1.0/2.0) h = Nu * k / L q = h * (Tplate-Twater)

print('Rate of Heat Transfer') print(str(q)+' W/m^2') (:sourceend:) (:divend:)

• Solution 1, Solution 2, Solution 3

• Assignment #4 - Excel Template - HR Data - Dynamics Data

• Solution 1, Solution 2, Solution 3

• Assignment #2 - Problem 2 Data File

• Solution 1, Solution 2, Solution 3

• Assignment #2 - Problem 2 Data File Δ

• Solution 1, Solution 2, Solution 3

• Assignment #2 - HW2, Problem 3 Data File Δ

• Assignment #2 - Problem 3 Data File Δ

• Solutions 1-7 and 8-10 (Python)

• Solutions 1-7 and 8-10 (Python)

• Solutions 1-7 (Python)
• Solutions 8-10 (Python)

• Assignment #18 - Variables and Equations (MathCAD) and (Python)
  • Solutions (MathCAD)

• File Input and Output

• Symbolic Derivatives and Integrals

• Conditionals
• Functions

• Generate Plots
• Data Analysis

• Solve Equations

• Data Regression

• Differential Equation Solution

• VBA Macros

• Conditionals
• Functions

• Loops
• Arrays

• Generate Plots

• User Interaction

• Debugging

• File Input and Output

• Classes (collections of values and functions)

• Solve Equations

• Data Regression

• Solve Differential Equations

• Symbolic Derivatives

• Assignment #1
  • Conditions and Functions
  • Solution 2, Solution 3
• Assignment #2
  • Generate Plots and Data Analysis
  • Solution 1, Solution 2, Solution 3
• Assignment #3
  • Solve Equations
  • Solution 1, Solution 2, Solution 3, Solution 4
• Assignment #4 - Files
  • Regression
  • Solution 2, Solution 3
• Assignment #5
  • Differential Equations
  • Solution 1, Solution 2
• Assignment #6
  • VBA Macros
  • Solution 1, Solution 2

• Conditionals, Functions

• Conditionals, Functions

• Assignment #8 - Files
  • Loops and Arrays
  • Solutions
• Assignment #9 - Files
  • Plot Data and Correlations
  • Solution 1, 2, 3, 4
• Assignment #10
  • User input, loops, ODEs, and plotting
  • Solution 2 (Video) - Solution 2 (.py) - Solution 3
• Assignment #11 - Files
  • Debugging
  • Solutions (1-5), Solution 6
• Assignment #12 - Files
  • File Input and Ouput
  • Solutions
• Assignment #13 - Files
  • Classes (collections of values and functions)
  • Solutions 1 - Solutions 2
• Assignment #14 - Files
  • Solve Equations (fsolve)
  • Solutions 1-2, Solution 3, Solution 4
• Assignment #15 - Files
  • Integration, interpolation, and regression
  • Solution 1, Solutions 2-4
• Assignment #16 - Files
  • Solve differential equations (odeint)
  • Solution
• Assignment #17 - Files
  • Symbolic solutions (sympy)
  • Solution

• Assignment #7 - Files
  • Conditionals, Functions, Loops
  • Solution 1, 2, 3, 4

• Assignment #5 - Solution 1, Solution 2

• Assignment #3 - Solution 1, Solution 2, Solution 3, Solution 4

• Assignment #6 - Solution 1, Solution 2

• Assignment #6 - Solution 2

• Assignment #5 - Solution 1

• Assignment #5

• Assignment #4 - Files - Solution 2, Solution 3

• Assignment #4 - Files - Solution 2,Solution 3

• Assignment #4 - Files - Solution 3

• Assignment #3 - Solution 1, Solution 2, Solution 3

• Assignment #1 - Solution 2, Solution 3

• Assignment #3 - Solution 1, Solution 2

• Assignment #3 - Solution 1

• Assignment #2 - Solution 1, Solution 2, Solution 3

• Assignment #2 - Solution 1, Solution 2, Solution 3

• Assignment #2 - Solution 1, 2, 3

• Assignment #2 - Solution 1

• Assignment #1 - Solution 2 - Solution 3

• Assignment #1 - Solution 2 - Solution 3

• Assignment #7 - Files - Solution 1, 2, 3, 4
• Assignment #8 - Files - Solutions
• Assignment #9 - Files - Solution 1, 2, 3, 4
• Assignment #10 - Solution 2 (Video) - Solution 2 (.py) - Solution 3
• Assignment #11 - Files - Solutions (1-5), Solution 6
• Assignment #12 - Files - Solutions
• Assignment #13 - Files - Solutions 1 - Solutions 2
• Assignment #14 - Files - Solutions 1-2, Solution 3, Solution 4
• Assignment #15 - Files - Solution 1, Solutions 2-4
• Assignment #16 - Files - Solution
• Assignment #17 - Files - Solution

• Solutions

• Solutions

• Solutions 1-6
• Solutions 7-9

(:title Programming Assignments:) (:keywords homework, nonlinear, optimization, engineering optimization, Excel, Mathcad, Visual Basic, MATLAB, differential, algebraic, modeling language, university course:) (:description Assignments for Problem-Solving Techniques for Chemical Engineers at Brigham Young University:)

Excel Assignments

• Assignment #1
• Assignment #2
• Assignment #3
• Assignment #4 - Files
• Assignment #5
• Assignment #6

Python Assignments

• Assignment #7 - Files
• Assignment #8 - Files
• Assignment #9 - Files
• Assignment #10
• Assignment #11 - Files
• Assignment #12 - Files
• Assignment #13 - Files
• Assignment #14 - Files
• Assignment #15 - Files
• Assignment #16 - Files
• Assignment #17 - Files

MathCAD Assignments

• Assignment #18 - Variables and Equations
• Assignment #19 - Functions, Arrays, and Logical Conditions
  • Download array.txt for import
• Assignment #20 - Symbolic Manipulation, Plots, and Solve Blocks
• Assignment #21 - Curve Fitting, Advanced Plots, and More on Solve Blocks
  • Assignment #21 - Supplemental Files