Optimal Acid Rain Abatement Strategies for Eastern Canada

In the past environmental management practices have been based on disparate analysis of the impacts of pollutants on selected components of ecosystems. However, holistic analysis of emission reduction strategies is necessary to justify that actions taken to protect the environment would not unduly punish economic growth or vice versa. When environmental management programs are implemented, it would be extremely difficult for the industry to attain the targeted emission reduction in a single year in order to eliminate impacts on ecosystems. It means that targets have to be established as increments or narrowing the gap between the desired level of atmospheric deposition and actual deposition. These targets should also be designed in a way that would balance the impacts on the economy with improvements in environmental quality. Environment Canada in partnership with other organizations has developed an Integrated Assessment Modeling Platform. This platform enables to identify an emission reduction strategy(ies) that is(are) able to attain the desired environmental protection at a minimum cost to the industry. In this study, an attempt is made to examine the impact on the industry when the level of protection provided to the aquatic ecosystems is implemented using environmental and environmental-economic goals as objectives using Canadian IAM platform. The modeling platform takes into account sources and receptor regions in North America. The results of the analysis indicated that reductions of at least 50% of depositions of SO2 would require complete removal of emissions from all sources. However, this is not compatible with the paradigm of balancing economy with the environment. Therefore, gradual reductions in emissions as well as depositions were found to be plausible strategy. Furthermore, optimization using only a single receptor at a time resulted in significantly higher reduction in emissions compared to optimization that incorporates all the twelve Canadian receptors in a single run. It implies that globally optimal emission reduction strategy (i.e., multi-receptor optimization) would not penalize the sources of emission compared to locally optimal emission reduction strategy (i.e., single receptor optimization). It is hoped that with this kind of analysis of feasible environmental targets can be put in place without jeopardizing the performance of the economy or industry while ensuring continual improvements in environmental health of ecosystems.Canada; long-range transport; air pollutants; acid deposition; North America; sources-receptors; negotiation; cost; emissions; cost functions; SO2; cost curves; control technologies; Integrated Assessment Modelling; USA

Pavement maintenance optimization strategies for national road network in Indonesia applying genetic algorithm

"6th International Workshop on Performance, Protection & Strengthening of Structures under Extreme Loading, PROTECT2017, 11-12 December 2017, Guangzhou (Canton), China"In Indonesia, the national road has an important role to increase the region's economy, the road has the function to preserve interprovincial or inter-provincial and regencies/cities. Road network in Indonesia has a significant length, of approximately 516,239 kilometers, where the majority presents lack of information related to monitoring data and evaluation. As a consequence, road maintenance is not appropriated. The objective of this paper is to describe the development of a Genetic Algorithm (GA) based on multi objectives programming of pavement and to investigate the optimal maintenance strategy options applied as function of road surface distress conditions. This is supported by database of an Integrated Road Management System (IRMS) and taking into account of both road network condition and agency costs. The optimization strategies provided by the developed soft computing tool can help solving agency problems; minimizing costs and maximizing road services.The Authors are grateful to the Lembaga Pengelola Dana Pendidikan (LPDP) for the financial support provided to this thesis through the grant financed by the Ministry of Finance Republic of Indonesia.info:eu-repo/semantics/publishedVersio

Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville-Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) - a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation-storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California

Intelligent Approaches For Modeling And Optimizing Hvac Systems

Advanced energy management control systems (EMCS), or building automation systems (BAS), offer an excellent means of reducing energy consumption in heating, ventilating, and air conditioning (HVAC) systems while maintaining and improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. This research will evaluate model-based optimization processes (OP) for HVAC systems utilizing MATLAB, genetic algorithms and self-learning or self-tuning models (STM), which minimizes the error between measured and predicted performance data. The OP can be integrated into the EMCS to perform several intelligent functions achieving optimal system performance. The development of several self-learning HVAC models and optimizing the process (minimizing energy use) will be tested using data collected from the HVAC system servicing the Academic building on the campus of NC A&T State University. Intelligent approaches for modeling and optimizing HVAC systems are developed and validated in this research. The optimization process (OP) including the STMs with genetic algorithms (GA) enables the ideal operation of the building’s HVAC systems when running in parallel with a building automation system (BAS). Using this proposed optimization process (OP), the optimal variable set points (OVSP), such as supply air temperature (Ts), supply duct static pressure (Ps), chilled water supply temperature (Tw), minimum outdoor ventilation, reheat (or zone supply air temperature, Tz), and chilled water differential pressure set-point (Dpw) are optimized with respect to energy use of the HVAC’s cooling side including the chiller, pump, and fan. HVAC system component models were developed and validated against both simulated and monitored real data of an existing VAV system. The optimized set point variables minimize energy use and maintain thermal comfort incorporating ASHRAE’s new ventilation standard 62.1-2013. The proposed optimization process is validated on an existing VAV system for three summer months (May, June, August). This proposed research deals primarily with: on-line, self-tuning, optimization process (OLSTOP); HVAC design principles; and control strategies within a building automation system (BAS) controller. The HVAC controller will achieve the lowest energy consumption of the cooling side while maintaining occupant comfort by performing and prioritizing the appropriate actions. Recent technological advances in computing power, sensors, and databases will influence the cost savings and scalability of the system. Improved energy efficiencies of existing Variable Air Volume (VAV) HVAC systems can be achieved by optimizing the control sequence leading to advanced BAS programming. The program’s algorithms analyze multiple variables (humidity, pressure, temperature, CO2, etc.) simultaneously at key locations throughout the HVAC system (pumps, cooling coil, chiller, fan, etc.) to reach the function’s objective, which is the lowest energy consumption while maintaining occupancy comfort

Disaggregated Imaging Spacecraft Constellation Optimization with a Genetic Algorithm

This research is an extension of work by Major Robert Thompson, who uses a genetic algorithm to optimize certain parameters of a disaggregated constellation for most cost-effective coverage. This work looks at imaging sensor coverage of a specific target deck assumed to exist in the Middle East. Parameters varied in this optimization affect Walker constellation characteristics, orbital elements, and sensor size. Walker parameter variables are number of planes, number of satellites per plane, true anomaly spread, and RAAN increment. All classical orbital elements are variable, although a circular, low-Earth orbit is assumed. Sensor size is varied dependent upon sensor diameter. These parameters are applied to constellations of small satellites and large satellites. The Unmanned Spacecraft Cost Model (USCM) and the Small Spacecraft Cost Model (SSCM) are used to roughly determine the cost of each proposed mission. The sensor effectiveness is determined by the General Imaging Quality Equation (GIQE)

Pharmacogenomics in children: advantages and challenges of next generation sequencing applications

Pharmacogenetics is considered as a prime example of how personalized medicine nowadays can be put into practice. However, genotyping to guide pharmacological treatment is relatively uncommon in the routine clinical practice. Several reasons can be found why the application of pharmacogenetics is less than initially anticipated, which include the contradictory results obtained for certain variants and the lack of guidelines for clinical implementation. However, more reproducible results are being generated, and efforts have been made to establish working groups focussing on evidence-based clinical guidelines. For another pharmacogenetic hurdle, the speed by which a pharmacogenetic profile for a certain drug can be obtained in an individual patient, there has been a revolution in molecular genetics through the introduction of next generation sequencing (NGS), making it possible to sequence a large number of genes up to the complete genome in a single reaction. Besides the enthusiasm due to the tremendous increase of our sequencing capacities, several considerations need to be made regarding quality and interpretation of the sequence data as well as ethical aspects of this technology. This paper will focus on the different NGS applications that may be useful for pharmacogenomics in children and the challenges that they bring on

Navigation Constellation Design Using a Multi-Objective Genetic Algorithm

In satellite constellation design, performance and cost of the system drive the design process. The Global Positioning System (GPS) constellation is currently used to provide positioning and timing worldwide. As satellite technology has improved over the years, the cost to develop and maintain the satellites has increased. Using a constellation design tool, it is possible to analyze the tradeoffs of new navigation constellation designs (Pareto fronts) that illustrate the tradeoffs between position dilution of precision (PDOP) and system cost. This thesis utilized Satellite Tool Kit (STK) to calculate PDOP values of navigation constellations, and the Unmanned Spacecraft Cost Model (USCM) along with the Small Spacecraft Cost Model (SSCM) to determine system cost. The design parameters used include Walker constellation parameters, orbital elements, and transmit power. The results show that the constellation design tool produces realistic solutions. Using the generated solutions, an analysis of the navigation constellation designs was presented