Novel approach for optimal process design under uncertainty

“…In this approach, the bounded uncertain variables are discretised into multiple intervals such that each individual interval represents a scenario with an approximated discrete distribution (Halemane and Grossmann 1983;Pistikopoulos and Ierapetritou 1995;Rooney and Biegler 1999;Subrahmanyam et al 1994). The solution obtained is desirable as its feasibility is assessed for all potential scenarios considered.…”

Section: Mathematical Optimisationmentioning

confidence: 99%

State-Of-The-Art Review of Mathematical Optimisation Approaches for Synthesis of Energy Systems

Andiappan

2017

Process Integr Optim Sustain

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An energy system is a crucial component in fulfilling the energy requirements of a given industrial process. If not designed appropriately, energy systems may not be able to perform designated operations in an optimised manner. Mathematical optimisation approaches have had a long history in addressing the synthesis of energy systems. Mathematical optimisation approaches are part of a larger domain known as process systems engineering (PSE). The main objective of this review is to provide a state-of-the-art overview of the mathematical optimisation approaches developed, particularly those developed for synthesis of energy systems, including the handling of uncertainty and the optimisation of multiple objectives. Subsequently, the synthesis of energy systems is further discussed on specific areas such as reliability, operability, flexibility and retrofit and eco-industrial parks. Following this, an overall analysis of the contributions in these areas is provided. Finally, future research directions are identified at the end of this review.

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“…Nowadays, there exist powerful solution techniques to solve stochastic mixed integer programming problems [100]. Successful applications of this techniques to scheduling problems in the chemical process industry are reported, for instance, by Sand et al Process design optimization under uncertainty has also a long history in the process industry (cf., [55], [97], [26]). Even global optimization techniques have been applied to design under uncertainty (cf., [56] or [41]).…”

Section: Customer Portfolio Optimizationmentioning

confidence: 99%

Solving Planning and Design Problems in the Process Industry Using Mixed Integer and Global Optimization

Kallrath

2005

Ann Oper Res

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This contribution gives an overview on the state-of-the-art and recent advances in mixed integer optimization to solve planning and design problems in the process industry. In some case studies specific aspects are stressed and the typical difficulties of real world problems are addressed.Mixed integer linear optimization is widely used to solve supply chain planning problems. Some of the complicating features such as origin tracing and shelf life constraints are discussed in more detail. If properly done the planning models can also be used to do product and customer portfolio analysis.We also stress the importance of multi-criteria optimization and correct modeling for optimization under uncertainty. Stochastic programming for continuous LP problems is now part of most optimization packages, and there is encouraging progress in the field of stochastic MILP and robust MILP.Process and network design problems often lead to nonconvex mixed integer nonlinear programming models. If the time to compute the solution is not bounded, there are already a commercial solvers available which can compute the global optima of such problems within hours. If time is more restricted, then tailored solution techniques are required.

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Novel approach for optimal process design under uncertainty

Cited by 194 publications

References 34 publications

New approach for quantifying process feasibility: Convex and 1‐D quasi‐convex regions

New approach for quantifying process feasibility: Convex and 1‐D quasi‐convex regions

State-Of-The-Art Review of Mathematical Optimisation Approaches for Synthesis of Energy Systems

Solving Planning and Design Problems in the Process Industry Using Mixed Integer and Global Optimization

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