Resource Constrained Optimal Operation of Industrial Processes
Rohit Omar (Dr. Munawar Shaik)
12th April 2017, Wednesday, 04:30‐05:00 pm (25+5 mins), ChE Seminar hall
Continuous and semi-continuous plants cater to large demands to produce different fast moving consumer goods (FMCG), petrochemicals, polymers and fertilizer products. Make and pack production processes handle products that are consumed immediately and sold in large quantities. Several authors have solved scheduling problems in this area with different modeling approaches. However, there are still challenging concerns for large scale plants. Among the different time representations available, unit-specific event-based continuous time representation has evolved as the most efficient formulation. The goal of this study is to generalize and enhance previous scheduling models for better handling of sequencing constraints and different storage policies such as unlimited intermediate storage (UIS), no intermediate storage (NIS), and finite intermediate storage (FIS).
In this study, we present an extension of an earlier model for short-term scheduling of continuous/semi-continuous plants based on conditional sequencing, where a consumption task is conditionally aligned to a production task only if it receives material from production task, otherwise if material is received from storage then sequencing is relaxed. Additional four-index binary and continuous variables are introduced to handle conditional sequencing by modifying the material balances and sequencing constraints in the earlier models. The conditional sequencing model has been extended to handle planned maintenance operation. Using different instances of a motivating example long with literature examples the proposed approaches are validated resulting in improved objective function value and/or the converged objective value is found using less number of events. The unified model has been extended to mixed batch and continuous processes and it has been tested on two case studies resulting in better objective function.