Non-linear carbon dioxide determination using infrared gas sensors and neural networks with Bayesian regularization

Carbon dioxide gas concentration determination using infrared gas sensors combined with Bayesian regularizing neural networks is presented in this work. Infrared sensor with a measuring range of 0~5% was used to measure carbon dioxide gas concentration within the range 0~15000 ppm. Neural networks were employed to fulfill the nonlinear output of the sensor. The Bayesian strategy was used to regularize the training of the back propagation neural network with a Levenberg-Marquardt (LM) algorithm. By Bayesian regularization (BR), the design of the network was adaptively achieved according to the complexity of the application. Levenberg-Marquardt algorithm under Bayesian regularization has better generalization capability, and is more stable than the classical method. The results showed that the Bayesian regulating neural network was a powerful tool for dealing with the infrared gas sensor which has a large non-linear measuring range and provide precise determination of carbon dioxide gas concentration. In this example, the optimal architecture of the network was one neuron in the input and output layer and two neurons in the hidden layer. The network model gave a relationship coefficient of 0.9996 between targets and outputs. The prediction recoveries were within 99.9~100.0%

Compact hollow waveguide mid-infrared gas sensor for simultaneous measurements of ambient CO2 and water vapor

A compact, sensitive and stable hollow waveguide (HWG) mid-infrared gas sensor, based on gas absorption lines using wavelength modulation spectroscopy with a second harmonic (WMS-2f) detection scheme, was developed for simultaneous measurements of ambient CO 2 and water vapor. Optimization of the laser modulation parameters and pressure parameter in the HWG are performed to improve the strength of the WMS-2f signal and hence the detection limit, where 14.5-time for CO 2 and 8.5-time for water vapor improvement in system detection limit is achieved compared to those working at 1 atm. The stability of the sensor has been improved significantly by optimizing environmental disturbances, incoupling alignment of the HWG and laser scanning frequency. An Allan variance analysis shows detection limit of the developed sensor of ~3 ppmv for CO 2 and 0.018% for water vapor, which correspond to an absorbance of 2.4 × 10 -5 and 2.7 × 10 -5 , with a stability time of 160 s, respectively. Ambient CO 2 and water vapor measurement have been performed in two days in winter and spring separately. The measurement precision is further improved by applying a Kalman adaptive filter. The HWG gas sensor demonstrates the ability in environmental monitoring and the potential to be used in other areas, such as industrial production and biomedical diagnosis

High-Performance Integrated Window and Façade Solutions for California

The researchers developed a new generation of high-performance façade systems and supporting design and management tools to support industry in meeting California’s greenhouse gas reduction targets, reduce energy consumption, and enable an adaptable response to minimize real-time demands on the electricity grid. The project resulted in five outcomes: (1) The research team developed an R-5, 1-inch thick, triplepane, insulating glass unit with a novel low-conductance aluminum frame. This technology can help significantly reduce residential cooling and heating loads, particularly during the evening. (2) The team developed a prototype of a windowintegrated local ventilation and energy recovery device that provides clean, dry fresh air through the façade with minimal energy requirements. (3) A daylight-redirecting louver system was prototyped to redirect sunlight 15–40 feet from the window. Simulations estimated that lighting energy use could be reduced by 35–54 percent without glare. (4) A control system incorporating physics-based equations and a mathematical solver was prototyped and field tested to demonstrate feasibility. Simulations estimated that total electricity costs could be reduced by 9-28 percent on sunny summer days through adaptive control of operable shading and daylighting components and the thermostat compared to state-of-the-art automatic façade controls in commercial building perimeter zones. (5) Supporting models and tools needed by industry for technology R&D and market transformation activities were validated. Attaining California’s clean energy goals require making a fundamental shift from today’s ad-hoc assemblages of static components to turnkey, intelligent, responsive, integrated building façade systems. These systems offered significant reductions in energy use, peak demand, and operating cost in California

Genetic algorithms with self-organizing behaviour in dynamic environments

Copyright @ 2007 Springer-VerlagIn recent years, researchers from the genetic algorithm (GA) community have developed several approaches to enhance the performance of traditional GAs for dynamic optimization problems (DOPs). Among these approaches, one technique is to maintain the diversity of the population by inserting random immigrants into the population. This chapter investigates a self-organizing random immigrants scheme for GAs to address DOPs, where the worst individual and its next neighbours are replaced by random immigrants. In order to protect the newly introduced immigrants from being replaced by fitter individuals, they are placed in a subpopulation. In this way, individuals start to interact between themselves and, when the fitness of the individuals are close, one single replacement of an individual can affect a large number of individuals of the population in a chain reaction. The individuals in a subpopulation are not allowed to be replaced by individuals of the main population during the current chain reaction. The number of individuals in the subpopulation is given by the number of individuals created in the current chain reaction. It is important to observe that this simple approach can take the system to a self-organization behaviour, which can be useful for GAs in dynamic environments.Financial support was obtained from FAPESP (Proc. 04/04289-6)

Variable Powder Flow Rate Control in Laser Metal Deposition Processes

This paper proposes a novel technique, called Variable Powder Flow Rate Control (VPFRC), for the regulation of powder flow rate in laser metal deposition processes. The idea of VPFRC is to adjust the powder flow rate to maintain a uniform powder deposition per unit length even when disturbances occur (e.g., the motion system accelerates and decelerates). Dynamic models of the powder delivery system motor and the powder transport system (i.e., five–meter pipe, powder dispenser, and cladding head) are first constructed. A general tracking controller is then designed to track variable powder flow rate references. Since the powder flow rate at the nozzle exit cannot be directly measured, it is estimated using the powder transport system model. The input to this model is the DC motor rotation speed, which is estimated on–line using a Kalman filter. Experiments are conducted to examine the performance of the proposed control methodology. The experimental results demonstrate that VPFRC is successful in maintaining a uniform track morphology, even when the motion control system accelerates and decelerates.Mechanical Engineerin