batchdes.gms : Optimal Design for Chemical Batch Processing

**Description**

A chemical batch process having three processing stages: mixing, reaction and centrifuge separation, produce two products. This model is used to determine the number and sizes of units to operate in parallel at each stage. The resulting model is nonlinear and mixed integer;

**References**

- Morari, M, and Grossmann, I E, Eds, Chemical Engineering Optimization Models with GAMS. Computer Aids for Chemical Engineering Corporation, 1991.
- Kocis, G R, and Grossmann, I E, Global Optimization of Nonconvex MINLP Problems in Process Synthesis. Industrial and Engineering Chemistry Research 27, 8 (1988), 1407-1421.

**Small Model of Type :** MINLP

**Category :** GAMS Model library

**Main file :** batchdes.gms

```
$title Optimal Design for Chemical Batch Processing (BATCHDES,SEQ=119)
$onText
A chemical batch process having three processing stages: mixing,
reaction and centrifuge separation, produce two products. This
model is used to determine the number and sizes of units to
operate in parallel at each stage. The resulting model is
nonlinear and mixed integer;
Morari, M, and Grossmann, I E, Eds, Chemical Engineering Optimization
Models with GAMS. Computer Aids for Chemical Engineering Corporation,
1991.
Kocis, G R, and Grossmann, I E, Global Optimization of Nonconvex MINLP
Problems in Process Synthesis. Independent Engineering Chemical
Research 27 (1988), 1407-1421.
Keywords: mixed integer nonlinear programming, chemical engineering, chemical
industry, chemical batch process, process designment
$offText
Set
i 'products' / a, b /
j 'stages' / mixer, reactor, centrifuge /
k 'potential number of parallel units' / 1*3 /;
Scalar
h 'horizon time (available time hrs)' / 6000. /
vlow 'lower bound for size of batch unit' / 250. /
vupp 'upper bound for size of batch unit' / 2500. /;
Parameter
q(i) 'demand of product i'
/ a 200000
b 150000 /
alpha(j) 'cost coefficient for batch units'
/ mixer 250.
reactor 500.
centrifuge 340. /
beta(j) 'cost exponent for batch units'
/ mixer 0.6
reactor 0.6
centrifuge 0.6 /
* The parameters below are defined to obtain the original variables
actv(j) 'actual volume (l)'
actb(i) 'actual batch sizes (kg)'
acttl(i) 'actual cycle times (hrs)'
actn(j) 'actual number of units in parallel'
coeff(k) 'represent number of parallel units';
coeff(k) = log(ord(k));
Table s(i,j) 'size factor for product i in stage j (kg per l)'
mixer reactor centrifuge
a 2 3 4
b 4 6 3;
Table t(i,j) 'processing time of product i in batch j (hrs)'
mixer reactor centrifuge
a 8 20 4
b 10 12 3;
Variable
y(k,j) 'binary variable denoting stage existence'
v(j) 'volume of stage j (l)'
b(i) 'batch size of product i (kg)'
tl(i) 'cycle time of product i (hrs)'
n(j) 'number of units in parallel stage j'
cost 'total cost of batch processing units ($)';
Binary Variable y(k,j);
Positive Variable v(j), b(i), tl(i), n(j);
Equation
vol(i,j) 'calculate volume of stage j'
cycle(i,j) 'calculate cycle time of product i'
time 'time constraint'
units(j) 'calculate number of processing units per stage'
lim(j) 'limit selection to one number'
obj 'objective function definition';
* Convexified formulation of model equations
* Volume requirement in stage j
vol(i,j).. v(j) =g= log(s(i,j)) + b(i);
* Cycle time for each product i
cycle(i,j).. n(j) + tl(i) =g= log(t(i,j));
* Constraint for production time
time.. sum(i, q(i)*exp(tl(i) - b(i))) =l= h;
* Relating number of units to 0-1 variables
units(j).. n(j) =e= sum(k , coeff(k)*y(k,j));
* Only one choice for parallel units is feasible
lim(j).. sum(k, y(k,j)) =e= 1;
* Defining objective function
obj.. cost =g= sum(j, alpha(j)*(exp(n(j) + beta(j)*v(j))));
Model batch / all /;
* bounds section
v.lo(j) = log(vlow);
v.up(j) = log(vupp);
n.up(j) = log(3.);
* tight lower bounds are computed below for cycle times and batch sizes
Parameter
tllow(i) 'lower bound on tl(i)'
tlupp(i) 'upper bound on tl(i)';
tllow(i) = smax(j, t(i,j)/exp(n.up(j)));
tlupp(i) = smax(j, t(i,j));
tl.lo(i) = log(tllow(i));
tl.up(i) = log(tlupp(i));
Parameter
blow(i) 'lower bound on b(i)'
bupp(i) 'upper bound on b(i)';
blow(i) = q(i)*(smax(j, t(i,j)/exp(n.up(j))))/h;
bupp(i) = min(q(i), smin(j, exp(v.up(j))/s(i,j)));
b.lo(i) = log(blow(i));
b.up(i) = log(bupp(i));
* initial point
* binary variables set for 3 units per stage
y.l(k,j) = 0;
y.l('3','mixer') = 1;
y.l('3','reactor') = 1;
y.l('3','centrifuge') = 1;
n.l(j) = sum(k, coeff(k)*y.l(k,j));
* batch sizes are set at the mid-point between bounds
b.l(i) = (b.up(i) + b.lo(i))/2;
v.l(j) = smax(i,log(s(i,j)) + b.l(i));
tl.l(i) = smax(j,log(t(i,j)) - n.l(j));
solve batch using minlp minimizing cost;
* converting variables into original form
actv(j) = exp(v.l(j));
actb(i) = exp(b.l(i));
acttl(i) = exp(tl.l(i));
actn(j) = exp(n.l(j));
* optimal design
display actv, actb, acttl, actn;
```