1. Institute of Chemical engineering- Bulgarian academy of sciences
Optimal loading the systems for resource consumption in case of multiproduct and multipurpose batch chemical plants (theoretical aspects and software realization)
B.Ivanov, K.Mintchev
Institute of Chemical engineering- Bulgarian academy of sciences
ul. Acad. G.Bonchev, 103, Sofia 1113
Fax: +(359)(2)

2. These systems are the most frequently used in :
The objects which are considering using ECAM to solve the problem about operative management are:
Multipurpose batch chemical systems
2. Multiproduct batch chemical systems
Steam
plant
Electricity
Water
5 Batch size=250kg
Cycle time=15h 1 2 3 4 5
V=5m3
V=8m3
V=12m3
V=15m3
Technology 2
Technology 1
1 Batch size=250kg
3. Manufacture plants including Multiproduct and Multipurpose batch chemical systems, which have shared consumption systems
These systems are the most frequently used in :
Pharmaceutical industry
Fine chemical plants
Food industry
Paint and varnish manufacturing. 1

3. Multipurpose batch chemical systems
Technology 1
Technology 2
Multipurpose batch chemical systems
No=
Stage 1
Stage 2
Technology 1
S11= t11=2h
S12= t12=5h
Technology 2
S21= t21=7h
S22= t22=2h 1 4 6
V=5m3
V=15m3 2 5 1
V=5m3 4 1 3 1 2 5 5 3 4 3
V=8m3
1 Batch size=80kg
Cycle time=10h
1 Batch size=250kg
Cycle time=15h 1 3 2
2 Batch size=70kg
Cycle time=12h
2 Batch size=250kg
Cycle time=15h 3 5 4
V=15m3
3 Batch size=150kg
Cycle time=15h
3 Batch size=250kg
Cycle time=15h 3 4 1 4
4 Batch size=100kg
Cycle time=15h
4 Batch size=250kg
Cycle time=15h 4 5 3 5
5 Batch size=250kg
Cycle time=15h
5 Batch size=250kg
Cycle time=15h
Steam
plant
Electric
plant
Water 1 5
V=12m3 1

4. Production scheduling model MIN(ts1+tc1+ts2+tc2+...+ ts14+tc14)
Service
time S1
ts1 S2
S14
ts14
Campaign 1
tc1
5 Batch size=250kg
Cycle time=15h
1 Batch size=250kg
Campaign 2
tc2
4 Batch size=100kg
Campaign 14
tc14
1 Batch size=80kg
Cycle time=10h
H - Planning period /horizon/
G1 - Planed minimal quantity , which have to be manufactured for product 1
G2 - Planed minimal quantity , which have to be manufactured for product 2
The task of Optimal synthesis of manufacture scheduling is arrived to identify the campaigns, which are participate and working time, so that the optimal criteria to be satisfied. The criteria are:
1. Minimum time duration programm.
2. Maximum profit for planning horizon.
MIN(ts1+tc1+ts2+tc ts14+tc14)
MAX(P1r + P2r)
(ts1 + tc1 + ts2 + tc ts14 + tc14) < H
In case of performed the constraints :
G1r > G1)
G2r > G2) 4

5. Loading the consumption systems in case of manufacture campaigns
Steam
plant
Electric
Water
5 Batch size=250kg
Cycle time=15h 1 2 3 4 5 6
V=5m3
V=8m3
V=12m3
V=15m3
Technology 1
Technology 2
1 Batch size=250kg
No=
Stage 1
Stage 2
Tehnology 1
S11= t11=2h
S12= t12=5h
Tehnology 2
S21= t21=7h
S22= t22=2h
Optimal loading of steam station in case
of simultaneous work of two manufacture
Optimal start time of manufacture
Optimal wait time after each batch
Optimal batch size 5

6. 1 Batch size=250kg 5 Batch size=250kg 7 Steam plant Electric Water
Cycle time=15h 1 2 3 4 5
V=5m3
V=8m3
V=12m3
V=15m3
Technology 2
Technology 1
1 Batch size=250kg 7

7. Mathematical formulation of the problem DATA
1. Data Let be introduced the following sets:
Set I{i1,i2,…,iN} set of products
Set J{j1,j2,…,jN} set of stages
Set P{p1,p2,…,pJ} set of processes
Also the following data are known:
Stage duration can be calculated for each stage each product
The required demand for each product per a horizon H.
Time horizon H.
The cycle time can be calculated for each product Expression for connecting:
Max or Min batch size can be calculated for each product

8. 1. Shifting time for the entire product
Mathematical formulation of the problem CONTINUOUS VARIABLES
1. Shifting time for the entire product
These variables present the time of shifting the beginning
of each product with regard the horizon beginning.
2.The waiting time between different batches
3. Batch size
The batch size for each product could be changed in the pointed upper and lower boundaries determined from the technologies. These variables affect on the power of energy consumption and/or the time of waiting between batches.

9. Mathematical formulation of the problem CONSTRAINTS
The following constraints are introduced:
1.The batch size constraints:
2.The waiting time between different batches
Where Ni present the minimum batch numbers that must be manufactured into a time horizon H.
3.The products shifting time constraints
4. The production constraints
5. The horizon constraints
6. Energy consumption constraints for simultaneous manufacturing of products

10. by using the Fourier transformation as:
Mathematical formulation of the problem Mathematical model of energy consumption
The energy consumption curve for each stage of given product can be written,
by using the Fourier transformation as:
Where:
are the respective Fourier coefficients for the function distributing stage j o product I. These coefficients are introduced for the cases when the energy consumption has the constant power as it was shown on the pictures.

11. 1. Energy consumption of stage j of product I
Mathematical formulation of the problem Mathematical model of energy consumption
Based on the Fourier transformation of the functions of energy consummationswe are able to present the following functions.
1. Energy consumption of stage j of product I
Where:
2. Energy consummation of product I:
Where:

12. 3. Energy consumption of entire campaign:
Mathematical formulation of the problem Mathematical model of energy consumption
3. Energy consumption of entire campaign:
Where:
The constant part of the power of energy consumption distribution in case of simultaneous product processing presents the ideal case that must be reached.
The variable parts of the above functions present the variation of the real curve around the ideal one. For the variable parts the following is valid:

13. Mathematical formulation of the problem Objective function
Case1. The minimum variation of the real curve with regard to the ideal one to be ensured.
Case 2. The minimum of energy consumption over the given determined in advance level of energy consumption.
Case 3. Maximum profit of the product manufacturing

14. 1.Control problem - for a group of compatible batch products
The offered software package is created on the base of original author’s methods published in international journals. Its present version uses MATLAB 6.5 and is adopted for WINDOWS-XP.
The software package ECAM
solves two main problems:
1.Control problem - for a group of compatible batch products
2.Simulation problem - for such
of product group aiming to determine load parameters
of resources supply systems. 1

15. ECAM could be used for investigation of different manufacturing states and as well as for evaluation the affect of energy integration processes on the resources supply systems. Thus, the technology changes could be properly assessed that provides opportunities for creation of proper production schedules ensuring optimal load of resources supply systems. 2

16. Main principles during the build up of ECAM
The menus principle is used
The principle of choices the data is used so that the human mistakes be reduced.
Logical control about correctness the data
The results are visualized
All functions consist of subsidiary information ECAM working under Windows’XP area
“Matlab 6.50” is used to build up the product 7

17. Help RUN PROGRAMME Exit programme Information About authors
Information gives a detailing account about different task classes and objects in which ECAM can be used.
Information about authors and description of the theoretical base in which ECAM is made of.
After activating (push the button ) the necessary information
(data base) is charged in the operative memory (RAM).
ECAM is started.
Help
Information
About authors 6

18. INPUT DATA BATCH PLANTS VIZUALIZATION OF SHEDULING
INPUT DATA TECHNOLOGY
INPUT DATA CAMPAIGN
Module for creation a date base for a production system.
It ensures a proper description of the existing plant units and connection between them
OPTIMAL SHEDULING
Module for creation a date base for technologies.
It ensures description of different technologies available
For processing in the plant.
Module for formulation of different classes controlling problems.
It involves definition of criteria for optimal control and set of constraints.
VIZUALIZATION OF SHEDULING
Module for automatically generation or customization of production campaign (group of compatible products) available to fulfill a given production demands.
PRINT SHEDULING
Module for formulation of different classes controlling problems.
It involves definition of criteria for optimal control and set of constraints.
Module for graphical interface for visualization the results obtained under optimal scheduling of campaign. 7

19. -delete the apparatus; -edit data base of current apparatus;
Data for the apparatus and connection between each other in material flow.
Charging the data base about existing apparatus and connection between them.
Rule buttons:
-add new apparatus;
-delete the apparatus;
-edit data base of current apparatus;
-save data base of current apparatus;
-exit form this module;
-help.
1.Short name of plant.
2.Full name of plant.
3.Number of apparatus in Data Base.
Visualization of apparatus which include:
-short name of apparatus;
-image of apparatus;
-basic characterizations;
-image of type apparatus;
Creation the new connect or imaging of the existing connects between apparatus. 8

20. Information about resources: -quantity of each resource
Data technology.
Information about resources:
-quantity of each resource
required to one unit final product;
-apportionment of resources in time;
-consumption quantity for the stage.
Rule buttons:
-add new apparatus;
-delete the apparatus;
-edit data base ;
-save data base ;
-exit form this module;
-help.
Buttons which charge data base of chosen
technology:
-number of stages;
-obtainable profit for unit product.
Description the data for each stage of
technology:
-name of stages;
-time of stage;
-size factor;
-type of apparatus. 9

21. Rule module for synthesis tasks of production variants and campaign.
Manuel generation campaign;
Design of manufacturing campaigns which a determinate group of manufacture.
The program gives possibility to define the stages of each apparatus on given manufacture.
After successful defining of all stages and manufactures the next step is to calculate evaluation of each variant which are included in given campaign.
Automatically generation campaign maximum length
Define the available manufacturing campaign , each one consist of :
-maximal number manufactures which the manufacture program is made of.
For obtainable manufacture campaign evaluate are given and also the variants of every manufacture which are included in relevant campaign.
Automatically generation sets full campaign
Define maximal number of campaign in case that the manufacture are given.
For each variant of manufacture campaign variant which are consist of are given.
Generation of given technology.
Calculating available disposing in a given technology and parameters which are consist of:
-Minimum batch size;
-Maximum batch size;
-Cycle time;
-Minimum available manufacturing quantity;
-Maximum available manufacturing quantity; 10

22. Define the variants of disposing of given technology
List of apparatus which are disposing on every stage of technology of considering variant.
That also include information about:
-Maximum batch size;
-Minimum batch size;
-Cycle time;
-Maximal and minimum product demand.
Rule buttons:
-delete the technology;
-edit data technology ;
-save technology to data base ;
-exit form this module;
-print variant;
-help.
Define the variants in case of given data base.
Lead in the data base of technology:
-Short name of technology;
-Full name of technology;
-Number of stages.
Lead in the data base about planning horizon.
Lead in the data base about production demand:
-Minimum demand of product;
-Normal demand of product;
-Maximum demand of product;
Lead in the data base about work regime:
-Whit overlapping the cycle;
-Without overlapping the cycle. 11

23. Design the manufacturing campaign from the customer.
Rule buttons:
-add new campaign;
-delete campaign;
-edit data base;
-save to data base;
-exit;
-help.
Read the campaigns data base
Output information about the campaign:
-Maximum batch size;
-Minimum batch size;
-Cycle time whitout overlapping;
-Cycle time whit overlapping;
Vizualization of apparatus in which the stage will carry out.
Choise the existing campaign from data base or lead in the data for new one.
Choise of relevant product who will be manufactured simultanuasly in campaign.
Lead in the stage into relevant apparatus.
Choise tha apparatus in which the stage will carry out. 12

24. -edit data base of given campaign; -save to data base; -exit; -help.
Synthesis of variants of campaigns with relatable size to number of manufacture which are included in a plan.
Output information about obtained variant of campaign:
-list of apparatus which have to placed in relevant stage
-basic characterization:
-maximum batch size, minimum batch size;
-cycle time;
-maximum demand,minimum demand;
-maximum number of variants.
Rule buttons:
-delete campaign;
-edit data base of given campaign;
-save to data base;
-exit;
-help.
Lead in the data base of existing manufacture plan or including the new one.
Choice the regime of manufacture:
-With overlapping the cycle or without overlapping the cycle;
-Production demand for the product:
-minimum product quantity;
-normal product quantity;
-maximum product quantity;
-Include the planning horizont.
Calculating the all available campaigns which are consist of the maximum number manufacture working at the same time. 13

25. Synthesis of maximal number independent campaigns.
Generalize information of found campaign,
Gives the maximum number of in depended campaign.
Rule buttons:
-delete campaign;
-edit data base of given campaign;
-save to data base;
-exit;
-help.
Lead in the data base of existing manufacture plan or including the new one.
Choice the regime of manufacture:
-With overlapping the cycles or without overlapping the cycles;
-Production demand for the product:
-minimum product quantity;
-normal product quantity;
-maximum product quantity;
-Include the planning horizon.
Calculating the all available campaigns which are consist of the maximum number manufacture working at the same time. 14

26. -edit data base of given campaign; -save to data base; -exit; -help.
Output the result of procedure of synthesis of maximal number independent campaigns in given manufacture plan.
Choice the manufacture variant included in given campaign which will be show on.
Rule buttons:
-delete campaign;
-edit data base of given campaign;
-save to data base;
-exit;
-help.
List of apparatus in which have to placed the relevant stages.
Output the obtained results for given variants of campaign. 15

27. Module explaining different optimizing tasks.
Optimal scheduling.
Optimal scheduling multicriteria formulation.
1.Minimum time production plane.
2.Maximum profit.
Restriction:
1.Minimum demand;
2.Minimum resource.
Optimal use of resource.
Optimal sheduling for given variant of canpaign-one criteria formulation.
Optimal use of resource.
Optimal sheduling for full variant of canpaign.
Multicriteria formulation.
Optimal use of resource.
Optimal sheduling for full variant of canpaign.
One criteria formulation.
Optimal scheduling.
Optimal scheduling with possible one criteria.
1.Minimum time poduction plane.
2.Maximum profit.
Restriction:
1.Minimum demand;
2.Minimum resource.
Optimal use of resource.
Optimal sheduling for given variant of canpaign.
Multicriteria formulation. 16

28. Optimal loading the system. /formulation the task/
Define the optimization criteria for chosen campaign who consist of:
-resource for optimization;
-choice the regime:
-variation
-constant
Output the results:
-included technologies;
-optimal start time;
-optimal wait time;
-optimal batch size.
Of each manufacture
Define the restrictions which are consist of:
-restrictions about another resources;
-minimum and maximum quantity in case of constant;
-minimum and maximum quantity in case of variation.
Choise the variant of campaign who will search optimal rule, in case of choosen criteria and defineded restrictions.
Read the data base for existing plans and available campaign.
Define the planning horizon in which a given campaign have to be done.
Start the optimization procedure
Define the quantity demand of each product. 17

29. Optimal loading the system in case of given manufacture program.
Define the optimization criteria for chosen campaign who consist of:
-resource for optimization;
-choice the regime:
-variation
-constant
Output the results:
-included technologies;
-optimal start time;
-optimal wait time;
-optimal batch size.
Of each manufacture
Define the restrictions which are consist of:
-restrictions about another resources;
-minimum and maximum quantity in case of constant;
-minimum and maximum quantity in case of variation.
Choise the variant of campaign who will search optimal rule, in case of choosen criteria and defineded restrictions.
Read the data base for existing plans and available campaign.
Define the planning horizon in which a given campaign have to be done.
Define the quantity demand of each product.
Start the optimization procedure 18

30. Optimal loading the system in case of given manufacture program.
Lead in the nesesary information which are consist of:
-DB for existing plants;
-Planning horizon;
-Production demands for each product;
-Optimization criteria;
-Restrictions.
Output the optimal parameters of each campaign which are consist of:
-Service time;
-Time duration campaign;
-List of manufacture included in campaign;
-Start times;
-Wait time;
-Cycle time;
-Optimal batch size;
-Optimal batch number;
-Quantity which are manufactured during campaign. 20

31. Visualization of obtainable scheduling.
Summarize data :
-Batch size;
-Start time;
-variation;
-wait time;
-constant production;
-horizon
Choice the resource for visualization
Read data of manufacture plan.
Input the number of campaign
Choice the resource
Choice the solution which have to be vizualizated
Choice the production of given campaign
Visualization the sum consumption curve of chosen resource in case of simultaneous working of all productions in given campaign.
The average curve of loading
The real curve of loading
Summarize data :
-variation;
-max pic;
-min pic
Manual input the data :
-Batch size;
-Start time;
-wait time;
-horizon
The average curve of loading
The real curve of loading 19

32. Thank you for your attention