"Elsevier出版的计算机辅助技术书籍英文资料及版权信息"

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C. Dombayci et al. 10

drawing tool. After this, module 3 concludes with the information in Table 2; which

shows examples of interaction between the data for each formulation using normatives.

Table 1. Introduced data

Description Process Input Process Output Intermediate Material Unit Procedure

Entered values A, B, C P AB UP1, UP2

Table 2. Examples of normative rules and conclusions

Normative rule in developed

ontology

Printed Data Meaning in the formulation

Instances of ProcessInput concept A, B, C Raw material set in planning

Instances of

UnitProcedure.hasInput

UP1.A, UP1.B,

UP2.C, UP2.AB

Input material of unit procedures

in order to create mass balance in

scheduling formulation

Instances of

UnitProcedure.hasOutput

UP1.AB, UP2.P Output material for mass balance

2.4. Module 4: Formulations

A short-term scheduling formulation (Kondili et al., 1993) is used to maintain the

scenarios in results in Section 3. The idea is to use the same formulation and produce

different solutions according to the data flow coming from other modules and

requirements of the decision-making procedure. The future advancement for this module

is to have a general formulation in order to respond the introduced from this methodology

and solve the optimization problems.

3. Results

The methodology is implemented as a CAPE tool

and different scenarios are used to show the ability

of adaptability to other scenarios. The data for

solving the scenarios are introduced through the

user interface module. Introducing master recipe

instances are shown in Figure 4. Scenarios are

lanned for a multi-process cell area in a site. The

decision-making problem is constructed from an

area manager point of view and the production

lanning is supported by finding different

scheduling results. In these results, demand is

divided for each cell for separate solutions

(scenario 1 and scenario 2), demand is added up

for all the cells for total solution (scenario 3a), and

demand is divided for each cell for a one solution

(scenario 3b). Details of scenarios are given as

follows:

3.1. Scenario 1

Figure 4. Implemented interface view

The first scenario is the original problem from Kondili et al. (1993), and is called Process

Cell 1 (PC1).

3.2. Scenario 2

The second scenario is similar to the original problem in scenario 1, but max batch sizes

are increased and decreased by 10% in Process Cell 2 (PC2) and Process Cell 3 (PC3),

Integrated management of hierarchical levels: towards to a CAPE tool 11

respectively. Part of the structured data in XML files from scenario 1 (process input, STN)

are used directly for these new cells. These additional process cells are solved separately.

3.3. Scenario 3

Scenario 3 has the integrated solution of these 3 process cells and the formulation receives

all the input data for the solution. In scenario 3a demand is multiplied by 3 and no specific

process cell is addressed for production. In contrast, scenario 3b consists of mapping the

amount of demand and process outputs for each process cells according to process cell

capacity change. Furthermore, intermediate storage amount is triplet for these scenarios.

4. Discussions

Table 3 summarizes the results for an area manager. Each scenario is designed for

different decision-making procedures and is proposed to show the capability of the

methodology for complex cases. For instance, if the question is to assign process cells to

specific production orders, the manager chooses within scenario 1 and scenario 2 by

inspecting the optimal scheduling solutions by checking the make-span time from the

table. Scenario 3a contains the optimal scheduling data when the demand is not assigned

to specific process cells and scenario 3b considers the assigned demand to each process

cells for a more specific situation. Scenario 3a has the highest profit since the problem is

constructed monolithic and constraints on demand attendances are removed comparing

to scenario 3b. Furthermore, computational times are shown in the table. The required

computational effort is lower when the max batch sizes increases (scenarios 1 and 2).

Also the required effort is reduced when constraints on product demand are removed

(scenario 3a).

Table 3. Result of scenarios

Scenarios Process Cells Product Demand Make-span Profit CPUs

1 PC1

1 500 units 40 hour 13432

units

0.593

2 400 units 31 hour

PC2 (10%

more)

1 500 units 40 hour 13535

units

0.453

2 400 units 21 hour

PC3 (10%

less)

1 500 units 40 hour 13393

unit

0.889

2 400 units 21 hour

3a PC1, PC2, PC3

3ྶ500 units

40 hour

40705

units

0.125

3ྶ400 units

22 hour

PC1

1 500 units 40 hour

40602

units

0.562

2 400 units 25 hour

PC2

1 550 units 39 hour

2 440 units 33 hour

PC3

1 450 units 32 hour

2 360 units 30 hour

The proposed data management approach recognizes each decision-making procedure in

a similar way, however handles data according to the problem to be solved. In this work,

an area manager is considered, but this factor can be replaced by a planning formulation

and the same data will be shared by two different formulations using module 3. The

proposed procedure is to remove the limitations between the hierarchical levels in

production systems and to allow the solutions flexibility considering the interactions and

decision variables. However, it is necessary to develop and implement different

formulations in module 4 in order to solve and improve decision-making problems. In

addition, the short-term scheduling formulation is used for different purposes than the

reported results for to demonstrate capability of the methodology.

C. Dombayci et al.

When other related methods and this study are connected, work is in the same research

line with Muñoz (2011) in terms of using functionalities of ontological models and the

knowledge introducing and collecting methods are managed generally. Additionally, this

work is not only on the interfacing of different elements in production systems but also

the solutions of mathematical programmes compared with Vegetti and Henning, (2015).

Modelling in the ontology is the main issue in this methodology, and similarly connecting

this model with programming skills. In general, problems occur in these connections and

more difficulties are expected in the solving of large scale problems.

5. Conclusions

This paper proposes a methodology for integrated management of production systems. It

also presents a modular approach, and introduces a flexible way of managing production

in different process cells while incorporating the planning requirements. The data needed

to solve the different optimization problems in different production scenarios are

introduced to a general class of problem formulation through a single interface, and the

ontology determines the problem instance to be solved. The methodology showed

robustness and flexibility for developing more complex cases and may be adapted to use

different auxiliary tools (like sophisticated drawing tools to efficiently feed data to the

ontology).

Future work in this line involves developing a more general formulation to address other

classes of problems in hierarchical systems. Thus, extended formulations should be

implemented and the capacity of the methodology should be tested accordingly.

Additionally, exploring data base applications to connect module 1 with other modules

and investigating further data exchange applications will be investigated.

Acknowledgments

Financial support received from the Spanish Ministry of Economy and Competitiveness

and the European Regional Development Fund (research Project SIGERA, DPI2012-

37154-C02-01), the ‘Agencia de Gesti d’Ajuts Universitaris i de Recerca-AGAUR’ (2014

FI00305), the Mexican National Council for Science and Technology (CONACyT) and

the Research Group CEPEiMA (2014SGR1092), is fully appreciated.

References

BatchML. Batch Markup Language. Retrived September 2015 from:

http://www.mesa.org/en/BatchML.asp.

C. Dombayci, J. Farreres, H. Rodríguez, E. Muñoz, E. Capón-García, A. Espuña, M. Graells,

2015. On the process of building a process systems engineering ontology using a semi-

automatic construction approach. Comput-Aided Chem. Eng., 37, 941–946.

M. Fedorova, G. Sin, R. Gani, 2015. Computer-aided modelling template: Concept and

application. Comput Chem Eng, 85, 232-247.

E. Kondili, C.C. Pantelides, R.W. Sargent, 1993. A general algorithm for short term scheduling of

batch operations. Comput Chem Eng, 17, 211-227.

C. T. Maravelias, C. Sung, 2009. Integration of production planning and scheduling: Overview,

challenges and opportunities. Comput and Chem Eng, 33, 1919-1930.

E. Muñoz, E. Capon-Garcia, J. M. Lainez-Aguirrec, A. Espuña, L. Puigjaner, 2015. Supply chain

planning and scheduling integration using lagrangian decomposition in a knowledge

management environment. Comput Chem Eng, 72, 52-67.

E. Muñoz, 2011. Knowledge management technology for integrated decision support systems in

process industries. Phd, UPC.

M. Vegetti, G. Henning, 2015. An ontological approach to integration of planning and scheduling

activities in batch process industries. Comput-Aided Chem Eng, 37, 995-1000.

Zdravko Kravanja, Miloš Bogataj (Editors), Proceedings of the 26

European Symposium on

Computer Aided Process Engineering – ESCAPE 26

http://dx.doi.org/10.1016/B978-0-444-63428-3.50007-2

Mathematical Optimization of Real-time Waste

Incineration Scheduling in the Industry

Matteo L. Abaecherli,

Daniel Santos González,

Elisabet Capón-García,

Konrad Hungerbühler

ETH Zürich, HCI G138, Vladimir-Prelog-Weg 1, 8093 Zürich, Switzerland

matteo.abaecherli@chem.ethz.ch

Abstract

This work proposes a novel approach for handling transfer activities together with

storage and processing tasks in the field of waste incineration. Given a set of waste

streams, a detailed plant topology, short or medium-term strategy specifications, and

optimization targets, the developed model optimizes the schedule for the incineration of

liquid waste along a given time horizon, from a waste transfer but also economic and

sustainable point of view. Additionally, detailed waste transfer, storage, mixing and

incineration assignments along with their start and processing time are provided. Tests

in industry show that the obtained optimized schedules can efficiently support real-time

decisions, leading to a more cost efficient and sustainable waste incineration.

Keywords: Scheduling, Mathematical Optimization, Waste Management, Incineration,

Energy efficiency

1. Introduction

Increasingly stringent environmental regulations, the price volatility of fossil fuels and

carbon emissions regulations, force the industry to increase the efficiency of their

operations (Harjunkoski et al., 2014). This also includes downstream processes such as

waste management, which is a quite challenging task in all sectors of the chemical

industry (Poliakoff et al., 2002). Large chemical sites often have their own in-house

waste treatment facilities, which ensure an independent and adequate waste handling,

allowing at the same time for material and energy recovery. A well-thought and smart

schedule is required in order to achieve a smooth and efficient operation, avoiding

holdups in the production as well as constraint violations of both technical and

regulatory nature (Wassick, 2009). In previous works (Wassick, 2009; Abaecherli et al.,

2014; Abaecherli et al., 2015) it has been shown that mathematical optimization tools

can deliver improved storage, mixing and treatment strategies for liquid waste

incineration in industry. By doing so, the natural gas consumption can be reduced up to

60 %; thereby both costs and environmental burden of the incineration process can be

significantly decreased. Nevertheless, most decision-making processes in this field are

still based purely on empirics. Likewise, existing optimization models only generate

short and medium-term schedules with low temporal resolution and are consequently

not reliable in supporting daily routines in big industrial sites with strongly fluctuating

waste production. The goal of this work is to deliver a high-resolution, real-time

optimization tool for facilitating industrial waste incineration scheduling and thereby

reduce the overall process costs and environmental impact.

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