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
$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;