Optimization Methods in Modeling the Mechanical Properties of Heavy Steel Plates / Metody Optymalizacyjne W Modelowaniu Własności Mechanicznych Blach Grubych

2012 ◽  
Vol 57 (4) ◽  
pp. 971-979
Author(s):  
A.Z. Grzybowski
Keyword(s):  
Mechanical Properties ◽  
Steel Production ◽  
Optimization Methods ◽  
Linear Constraints ◽  
Steel Plates ◽  
Chance Constrained Programming ◽  
Optimization Approach ◽  
Industrial Manufacturing ◽  
Optimization Task ◽  
Constrained Programming

The paper is devoted to an optimization approach to a problem of statistical modeling of mechanical properties of heavy steel plates during a real industrial manufacturing process. The approach enables the manufacturer to attain a specific set of the final product properties by optimizing the alloying composition within the grade specifications. Because this composition has to stay in the agreement with earlier indicated specifications, it leads to the large system of linear constraints, and the problem itself can be expressed in the form of linear programming (LP) task. It turns out however, that certain of the constraints contain the coefficients which have to be estimated on the base of the data gathered in the production process and as such they are uncertain. Consequently, the initial optimization task should be modeled as so-called Chance Constrained Programming problem (CCP), which is a special class within the stochastic programming problems. The paper presents mathematical models of the optimization problem that result from both approaches and indicates differences which are important for the decision makers in the production practice. Some examples illustrating the differences in solutions resulting from LP and CCP models are presented as well. Although the statistical analysis presented in this paper is based on the data gathered in the ISD Czestochowa Steelworks, the proposed approach can be adopted in any other process of steel production.

Design Optimization of Power Plants by Considering Multiple Partial Load Operation Points

2007 ◽  
Author(s):  
Marc Ju¨des ◽  
George Tsatsaronis
Keyword(s):  
Design Optimization ◽  
Power Plants ◽  
Optimization Procedure ◽  
Optimization Methods ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Optimization Task ◽  
Operation Conditions ◽  
Partial Load ◽  
Conventional Optimization

The design optimization of complex energy conversion systems requires the consideration of typical operation conditions. Due to the complex optimization task, conventional optimization methods normally take into account only one operation point that is, in the majority of cases, the full load case. To guarantee good operation at partial loads additional operation conditions have to be taken into account during the optimization procedure. The optimization task described in this article considers altogether four different operation points of a cogeneration plant. Modelling requirements, such as the equations that describe the partial load behavior of single components, are described as well as the problems that occur, when nonlinear and nonconvex equations are used. For the solution of the resulting non-convex mixed-integer nonlinear programming (MINLP) problem, the solver LaGO is used, which requires that the optimization problem is formulated in GAMS. The results of the conventional optimization approach are compared to the results of the new method. It is shown, that without consideration of different operation points, a flexible operation of the plant may be impossible.

Integration of an Automatic Optimizer Functionality Into the Design Process of Industrial Steam Turbines

2012 ◽  
Author(s):  
Dimitri Drapkin ◽  
Franz Kores ◽  
Thomas Polklas
Keyword(s):  
Design Process ◽  
Optimization Methods ◽  
Simultaneous Optimization ◽  
Design Parameters ◽  
Optimization Approach ◽  
Steam Turbines ◽  
Particle Swarm Optimizer ◽  
Optimization Task ◽  
Wide Range ◽  
Optimization Parameters

Industrial steam turbines are mostly tailor made machinery, varying in a wide range of specifications. This feature introduces high requirements on the design process which has to be flexible, efficient and fast at the same time. Given live steam and design parameters as input, the geometry corresponding to the valid design scheme can be calculated together with the required thermodynamic, aerodynamic and mechanical characteristics. By variation of design parameters a design may be achieved which optimizes both, efficiency and cost. The optimization task is formulated mathematically, e.g. crucial optimization parameters, criteria for evaluation of different designs and other required constraints are selected. The structure of the resulting optimization problem is analyzed. Based on this analysis a modular optimization system design is proposed. The choice of Genetic Algorithms and Adaptive Particle Swarm Optimizer as optimization methods is discussed, recommendations for their proper use are given. A bicriterial optimization approach for a simultaneous optimization of efficiency and cost is developed.

Economics of coated paper production made from deinked pulp

TAPPI Journal ◽  
2013 ◽  
Vol 12 (4) ◽  
pp. 19-27
Author(s):  
PATRICK HUBER ◽  
LAURENT LYANNAZ ◽  
BRUNO CARRÉ
Keyword(s):  
Mechanical Properties ◽  
Optimization Methods ◽  
Coating Process ◽  
Coated Paper ◽  
Paper Production ◽  
Base Paper

The fraction of deinked pulp for coated paper production is continually increasing, with some mills using 100% deinked pulp for the base paper. The brightness of the coated paper made from deinked pulp may be reached through a combination of more or less extensive deinking, compensated by appropriate coating, to optimize costs overall. The authors proposed general optimization methods combined with Kubelka-Munk multilayer calculations to find the most economical combination of deinking and coating process that would produce a coated paper made from DIP, at a given target brightness, while maintaining mechanical properties.

Chance constrained programming with some non-normal continuous random variables

OPSEARCH ◽  
2020 ◽  
Vol 57 (4) ◽  
pp. 1281-1298
Author(s):  
D. K. Mohanty ◽  
Avik Pradhan ◽  
M. P. Biswal
Keyword(s):  
Random Variables ◽  
Chance Constrained Programming ◽  
Constrained Programming

scholarly journals Heuristic Optimization of Overloading Due to Electric Vehicles in a Low Voltage Grid

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6069
Author(s):  
Sajjad Haider ◽  
Peter Schegner
Keyword(s):  
Electric Vehicles ◽  
Low Voltage ◽  
Optimization Methods ◽  
Monte Carlo Analysis ◽  
Heuristic Optimization ◽  
Optimization Approach ◽  
Worst Case ◽  
Voltage Drops ◽  
The Individual ◽  
Nodal Voltage

It is important to understand the effect of increasing electric vehicles (EV) penetrations on the existing electricity transmission infrastructure and to find ways to mitigate it. While, the easiest solution is to opt for equipment upgrades, the potential for reducing overloading, in terms of voltage drops, and line loading by way of optimization of the locations at which EVs can charge, is significant. To investigate this, a heuristic optimization approach is proposed to optimize EV charging locations within one feeder, while minimizing nodal voltage drops, cable loading and overall cable losses. The optimization approach is compared to typical unoptimized results of a monte-carlo analysis. The results show a reduction in peak line loading in a typical benchmark 0.4 kV by up to 10%. Further results show an increase in voltage available at different nodes by up to 7 V in the worst case and 1.5 V on average. Optimization for a reduction in transmission losses shows insignificant savings for subsequent simulation. These optimization methods may allow for the introduction of spatial pricing across multiple nodes within a low voltage network, to allow for an electricity price for EVs independent of temporal pricing models already in place, to reflect the individual impact of EVs charging at different nodes across the network.

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