Robust control of speed and temperature in a power plant gas turbine

In this paper, an H(∞) robust controller has been designed for an identified model of MONTAZER GHAEM power plant gas turbine (GE9001E). In design phase, a linear model (ARX model) which is obtained using real data has been applied. Since the turbine has been used in a combined cycle power plant, its speed and also the exhaust gas temperature should be adjusted simultaneously by controlling fuel signals and compressor inlet guide vane (IGV) position. Considering the limitations on the system inputs, the aim of the control is to maintain the turbine speed and the exhaust gas temperature within desired interval under uncertainties and load demand disturbances. Simulation results of applying the proposed robust controller on the nonlinear model of the system (NARX model), fairly fulfilled the predefined aims. Simulations also show the improvement in the performance compared to MPC and PID controllers for the same conditions.     

Formation of homogenous nanofibers using silicon microneedle spinnerets

We report the use of a silicon microfabricated device as a new spinneret for electrospinning purposes. This device has been realized on silicon substrates using a deep reactive ion etching process. To make proper holes in the center of microneedles, a rotating angle deposition method followed by vertical etching of silicon is employed. By using these needles as fluid nozzles in the electrospinning process, poly vinyl alcohol solution with a concentration of 7?% has been converted into nanofibers. The formation of nanofibers has been investigated using field emission scanning electron microscopy. Using this process, nanofibers with a diameter of 100–200?nm are realized where the dispersion is less than 50?nm. Finally, the effects of needle size and the applied voltage have been investigated on the diameter of nanofibers.     

Molecular tracking: A concept for side-draw distillation column design

A simple approach is introduced to locate a side-draw tray for ternary and multi-component mixtures with middle boiling component(s) present in the system at trace levels. The concept is based on a probability function defined by the thermodynamic properties of the system. The advantage of this method over existing methods is the ability to quickly and efficiently provide a feasible configuration of the distillation unit without relying on rigorous optimization or trial and error approaches. Moreover, it provides an intuitive understanding of the movements of the middle boiling components in the column.     

Integrated design and control of reactive distillation processes using the driving force approach

Superior controllability of reactive distillation (RD) systems, designed at the maximum driving force (design-control solution) is demonstrated in this article. Binary or multielement single or double feed RD systems are considered. Reactive phase equilibrium data, needed for driving force analysis and design of the RD system, is generated through an in-house property prediction tool. Rigorous steady-state simulation is carried out in ASPEN plus in order to verify that the predefined design targets and dynamics are met. A multiobjective performance function is employed to evaluate the performance of the RD system in terms of energy consumption, sustainability metrics (total CO₂ footprint), and control performance. Controllability of the designed system is evaluated using indices like the relative gain array (RGA) and Niederlinski index (N_I ), to evaluate the degree of loop interaction, as well as through dynamic simulations using proportional-integral (PI) controllers and model predictive controllers (MPC). The design-control of the RD systems corresponding to other alternative designs that do not take advantage of the maximum driving force is also investigated. The analysis shows that the RD designs at the maximum driving force exhibit enhanced controllability and lower carbon footprint than the alternative RD designs.     

Quick integrative optimizers for minimizing the error of neural computing in pan evaporation modeling

To achieve an efficient methodology for approximating pan evaporation (E_P), this study offers two metaheuristic-integrated predictors. Shuffled complex evolution (SCE) and electromagnetic field optimization (EFO) are two of the fastest metaheuristic algorithms that are synthesized with artificial neural network (ANN). By doing this, the ANN is optimized in a noticeably shorter time compared to its integration with other metaheuristic techniques. Five-year climatic data of the Bakersfield station (California, USA) with an 80:20 ratio are used for developing and testing the methods. The proposed hybrids are implemented with appropriate population sizes (20 and 35 for the SCE and EFO, respectively) and their results are compared to a single ANN. Accuracy evaluation (correlation coefficients > 0.99) professed that the neural network with both conventional and sophisticated trainers is a competent approach for the E_P simulation. Besides, it was observed that the error of prediction by the ANN-SCE and ANN-EFO is 6.02 and 9.27% lower than the single ANN, respectively. Therefore, the used strategies can enhance the applicability of the ANN. The time elapsed in the optimization using SCE and EFO was 479.0 and 281.9 s, respectively. A comparison between these algorithms revealed that the EFO is both a faster and more accurate optimizer. The ANN-EFO is accordingly recommended as a new efficient model for predicting the E_P.

     

Microarray Image Processing and Quality Control

Image processing is an important stage of every microarray experiment. Reliability of this stage strongly influences the results of data analysis performed on extracted gene expressions. Multiple methods related to array recognition, spot segmentation and measurement extraction have emerged in this area over past several years. Currently there are various commercial and freeware packages available, which perform microarray image analysis. This paper attempts to review microarray image analysis as a whole and to make some experimental comparison of several computational schemes for signal segmentation and measurement extraction. Also we provide a detailed discussion of automated image quality control for use with microarray images.     

A 0.8 μm BiCMOS Gate Driver for IGBT Power Switch

This paper discusses the design and implementation of a monolithic gate driver for an Insulated Gate Bipolar Transistor (IGBT). The objective is to implement a high voltage (25 V) monolithic gate driver with a novel protection circuit in a conventional low-voltage (5 V) high-density (0.8 m) BiCMOS process. Extended drain MOS-FETs are used to implement the high-voltage capability in this design.     

Combined heat and power economic dispatch by mesh adaptive direct search algorithm

The optimal utilization of multiple combined heat and power (CHP) systems is a complex problem. Therefore, efficient methods are required to solve it. In this paper, a recent optimization technique, namely mesh adaptive direct search (MADS) is implemented to solve the combined heat and power economic dispatch (CHPED) problem with bounded feasible operating region. Three test cases taken from the literature are used to evaluate the exploring ability of MADS. Latin hypercube sampling (LHS), particle swarm optimization (PSO) and design and analysis of computer experiments (DACE) surrogate algorithms are used as powerful SEARCH strategies in the MADS algorithm to improve its effectiveness. The numerical results demonstrate that the utilized MADS–LHS, MADS–PSO, MADS–DACE algorithms have acceptable performance when applied to the CHPED problems. The results obtained using the MADS–DACE algorithm are considerably better than or as well as the best known solutions reported previously in the literature. In addition to the superior performance, MADS–DACE provides significant savings of computational effort.