Fixed some typos

Author: ataideneto1

AuxFunctions/figured.m

@@ -39,7 +39,7 @@ function save2pdfemb(ho,~)
 end
 
 function save2pdflocal(pdfFileName,dpi,handle)
-% Addapted from (c) Gabe Hoffmann, gabe.hoffmann@gmail.com
+% Adapted from (c) Gabe Hoffmann, gabe.hoffmann@gmail.com
 
 % Backup previous settings
 prePaperType = get(handle,'PaperType');

AuxFunctions/lagrange.m

@@ -29,7 +29,7 @@
 % Allocation
 P = zeros(np,1);
 
-% Calculate the lagrange polinomial in x -> P(x)
+% Calculate the lagrange polynomial in x -> P(x)
 for j = 1:ng
     l = ones(np,1);
     for i = 1:ng

Extractors.m

@@ -22,8 +22,8 @@
 %   N:  Number of Stages
 %   R:  Raffinate flow rate 
 %   E:  Extract flow rate  
-%   Xf: Molar fration feeding stage 1 at raffinate 
-%   Yf: Molar fration feeding stage N at extract 
+%   Xf: Molar fraction feeding stage 1 at raffinate 
+%   Yf: Molar fraction feeding stage N at extract 
 %   Ki: Equilibrium constants
 %
 %   Mass balance:
@@ -46,7 +46,7 @@
 %% Problem setup
 addpath('AuxFunctions')
 
-% Numer of stages
+% Number of stages
 N = 10;
 K = 0.25*ones(N,1);
 R = 1;

MPC_tank_reactor.m

@@ -3,7 +3,7 @@
 %   
 %   You'll learn:
 %       +: how to solve optimization problems
-%       +: How to apply a model predictice control with non linear models
+%       +: How to apply a model predictive control with nonlinear models
 %   
 %% The problem
 %   
@@ -12,6 +12,12 @@
 %               lb <   u  < ub
 %                0 < |du| < du_max
 %               
+%   About the process:
+%   CSTR with van de vusse reaction system
+%    A -> B
+%    B -> C
+%   2A -> D
+%   
 %   ============================================================
 %   Author: ataide@peq.coppe.ufrj.br
 %   homepage: github.com/asanet
@@ -33,7 +39,7 @@
 % The objective function parameters
 Hp = 1000;      % Prediction horizon
 nt = 50;        % Time sampling points
-P_sp = 1;       % Set point violetion penalty
+P_sp = 1;       % Set point violation penalty
 P_u = 1e6;      % Saturation penalty
 P_du = 1e6;     % Delta u penalty
 lb = 273.15;    % lower bound to u
@@ -60,7 +66,7 @@
 % Simulation span
 tspan = 1:3*Hp;
 
-% The intial u profile (must have Hc values)
+% The initial u profile (must have Hc values)
 u = [373.15 0 0 0]';
 umi = u(1);
 
@@ -109,13 +115,13 @@
     um(i+1) = u(1);
     umi = u(1);
 
-    % Disturbance in tal
+    % Disturbance in tau
     if i == fix(nsamples/2)
         Tf = 420;
     end
 end
 
-%% plot the data
+% Plot the data
 figure;
 subplot(211)
 h = plot(tm,ym(:,3),tm,spval*ones(nsamples,1),'LineWidth',1.5);
@@ -136,7 +142,7 @@
 
     function f = mpc_cost_function(u,ynow)
 
-        % Solve the model until the preditction horizon
+        % Solve the model until the prediction horizon
         ts1 = linspace(0,Hp,nt); 
         [t,y] = ode15s(@model,ts1,ynow,simulationOpt,u);
 
@@ -151,26 +157,26 @@
         % Delta u penalty
         f3 = -min(0, du_max - max(abs([umi- u(1); u(2:end)])));
 
-        % THe objective function
+        % The objective function
         f = P_sp*f1 + P_u*f2 + P_du*f3;
     end
 
     function dy = model(t,y,u)
         % Van de vusse reaction in a CSTR
         Ca = y(1);  Cb = y(2);  T = y(3);
 
-        % the regularization function
+        % The regularization function
         reg = 1/2 + tanh(p*(t - td))/2;
 
-        % control variable
+        % Control variable
         Tc = sum(u.*reg);
 
-        % reaction rates
+        % Reaction rates
         r1 = k1*exp(-E1/R/T)*Ca;
         r2 = k2*exp(-E2/R/T)*Cb;
         r3 = k3*exp(-E3/R/T)*Ca^2;
 
-        % mass balances
+        % Mass balances
         dCa = (Caf - Ca)/tau -r1 -2*r3;
         dCb = -Cb/tau + r1 - r2;
         dT  = (Tf - T)/tau -1/rho/cp*( UA/V*(T - Tc) + H1*r1 + H2*r2 +H3*r3 );

Parameter_estimation.m

@@ -9,10 +9,18 @@
 %% The problem
 %   
 %   Given a data set (xe,ye), find the parameters of a dynamical
-%   non linear model. By least squares hte problem is:
+%   non linear model. By least squares the problem is:
 %   
 %   minimize the sum of (ye_i - ycalc_i)^2
 %               
+%   About the process:
+%   CSTR with van de vusse reaction system
+%    A -> B
+%    B -> C
+%   2A -> D
+%   
+%   Estimate the Arrhenius pre-exponential factor and activation energies
+% 
 %   ============================================================
 %   Author: ataide@peq.coppe.ufrj.br
 %   homepage: github.com/asanet
@@ -57,7 +65,7 @@
 % Calculate ycalc with the estimated parameters for comparison
 tspan = linspace(0,te(end),100)';
 [tc,yc] = ode15s(@model,tspan,y0,odeopt,parEst);
-[tc2,yc2] = ode15s(@model,te,y0,odeopt,parEst);
+[~,yc2] = ode15s(@model,te,y0,odeopt,parEst);
 
 % Plot data
 close all

Reaction_diffusion.m

@@ -59,7 +59,7 @@
 opt = optimoptions(@fsolve,'TolFun',1e-10,'TolX',1e-10,'Display','iter-detailed',...
     'Algorithm','trust-region-reflective');
 
-% Passing the Sparty Pattern (Compare solution time with and without it)
+% Passing the Sparsity Pattern (Compare solution time with and without it)
 opt.JacobPattern = Jp;
 
 % Solver call

Steady_state_PFR.m

@@ -18,7 +18,7 @@
 %   C_i = P/(R*T)*F_i/Ft
 %   u*sum(C_i) = Ft
 % 
-%   Auxiliar equations
+%   Auxiliary equations
 %   cp*Ft = sum(F_i*cp_i)
 %   
 %   ============================================================

Tubular_reactor_2D.m

@@ -48,10 +48,10 @@
 % Operational parameters (play around)
 vmax = 1;
 vz = vmax*(1 - (r/R).^2);   % Velocity profile
-Dz = 0.5*0;                 % Diffusion coeficients
+Dz = 0.5*0;                 % Diffusion coefficients
 Dr = 0.01*0;
 k = 0.1*0;                  % Kinetic constant
-Cf = ones(Nr,1);            % Feed concetration
+Cf = ones(Nr,1);            % Feed concentration
 
 % The initial condition
 y0 = zeros(Nr*Nz,1);
@@ -100,7 +100,7 @@
 xlabel('Length')
 ylabel('Diameter')
 hcb = colorbar;
-hcb.Label.String = 'Concetration';
+hcb.Label.String = 'Concentration';
 colormap jet
 for ii = 1:Nt
     imagesc(z,[2*r;2*r],[flip(Csol(:,:,ii));Csol(:,:,ii)])