(104g) Planning and Scheduling Industrial Waste Management Using Knowledge Based Lagrangean Decomposition
Waste liquid effluents originated from industrial activities contain contaminants and certain species, such as organic solvents, with material and energy recovery potential. Therefore, not only does industrial waste management stand as an end-of-pipe problem, but also as a potential origin of resources, which can result insignificant benefits if material and energy integration strategies are used. The simultaneous planning and scheduling of industrial waste management allows improved resource utilization and reduced environmental impact. However, rigorous mathematical models usually result in large scale problems. In order to tackle the problem complexity, decomposition techniques based on information flows between a master and a set of sub-problems are widely applied (Maravelias and Sung, 2009, Calfa et al., 2013).
In this work, we exploit this capability of ontologies to address the optimal integration of planning and scheduling using a Lagrangean decomposition approach. Thus, ontologies improve information sharing and communication in the enterprise and can even represent holistic mathematical models facilitating the use of analytic tools and providing higher flexibility for model building. Therefore, the scheduling/planning sub-problems are created for the waste management facility and their dual solution information is shared by means of the ontological framework. Furthermore, this work presents a multi-period waste management multi-objective optimization, considering economic and environmental issues. The behavior of waste treatment units is included in the optimization problem as black-box models based on industrial practice.
A case study of the planning and scheduling of industrial waste facilities is presented to show the advantages and limitations of the proposed approach in a multi-objective environment considering economic and environmental issues using the decomposition strategy. The proposed framework leads to reduced computation effort and better solutions in terms of solution quality, since waste stream scheduling is integrated in decision-making.
Calfa, B. A., Agarwal, A., Grossmann, I. E., Wassick, J. M., 2013. Hybrid bilevel-lagrangean decomposition scheme for the integration of planning and scheduling of a network of batch plants. Industrial & Engineering Chemistry Research 52 (5), 2152-2167.
Maravelias, C. T., Sung, C., 2009. Integration of production planning and scheduling: Overview, challenges and opportunities. Computers & Chemical Engineering 33 (12), 1919-1930.
This paper has an Extended Abstract file available; you must purchase the conference proceedings to access it.
Do you already own this?
Log In for instructions on accessing this content.
|AIChE Graduate Student Members||Free|
|AIChE Undergraduate Student Members||Free|