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Model-based methods are increasingly used in all areas of biopharmaceutical process technology. They can be applied in the field of experimental design, process characterization, process design, monitoring and control. Benefits of these methods are lower experimental effort, process transparency, clear rationality behind decisions and increased process robustness. The possibility of applying methods...
In this paper, receding horizon control of large-scale sewage systems is addressed considering different modelling approaches, which include several inherent continuous/ discrete phenomena (overflows in sewers and tanks) and elements (weirs) in the system. This fact results in distinct behaviours depending on the dynamic state (flow/volume) of the network. These behaviours can not be neglected nor...
Urban network traffic is a complex, nonlinear, unstable system and is significantly affected by immeasurable factors. This paper presents an optimal model to the application of signal coordination for urban network based on real-time traffic volumes. It divides a large signalized network into several subgroups. The number of subgroups, phase-time and offsets can be optimized to achieve an optimal...
Numerous modeling and simulation software products have been developed in the last decades. Most of them have been created from scratch, often dedicated to single formalisms, single simulation algorithms, hardware platforms, or applications. But nevertheless each of these software products has to follow principles, and it has to contain techniques and elements to be usable for modeling and simulation...
In this paper, a multiobjective evolutionary algorithm (MOEA) to solve optimal reactive power (VAR) dispatch problem with flexible AC transmission system (FACTS) devices is presented. This nonlinear multiobjective problem (MOP) consists to minimize simultaneously real power loss in transmission lines and voltage deviation at load buses, by tuning parameters and location of FACTS. The constraints of...
The type of lscr1 norm regularization used in Lasso and related methods typically yields sparse parameter estimates where most of the estimates are equal to zero. We study a class of estimators obtained by applying a linear transformation on the parameter vector before evaluating the lscr1 norm. The resulting ldquotransformed Lassordquo yields estimates that are ldquosmoothrdquo in a way that depends...
This work deals with a parametric identification using the least square method based on the output error. An appropriate particle swarm optimization algorithm is applied as a technique to minimize the objective function in order to obtain simultaneously the electrical and the mechanical parameters. The validation and comparison of the two algorithms used are obtained from experiments carried out on...
In this paper a methodology of sizing optimization of the stand-alone hybrid energy systems is presented. This approach makes use of a deterministic algorithm to suggest, among a list of commercially available system devices, the optimal number and type of units ensuring that the total costs of the autonomous hybrid wind/PV/Diesel, wind/Diesel and PV/Diesel energy systems are minimized while guaranteeing...
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