Cost-Effective NOx Control in the Eastern United States

Reducing nitrogen oxide (NOx) emissions in the eastern United States has become the focus of efforts to meet ozone air quality goals and will be useful for reducing particulate matter (PM) concentrations in the future. This paper addresses many aspects of the debate over the appropriate approach for obtaining reductions in NOx emissions from point sources beyond those called for in the Clean Air Act Amendments of 1990. Data on NOx control technologies and their associated costs, spatial models linking NOx emissions and air quality, and benefit estimates of the health effects of changes in ozone and PM concentrations are combined to allow an analysis of alternative policies in thirteen states in the eastern United States. The first part of the study examines the cost and other consequences of a command-and-control approach embodied in the Environmental Protection Agency’s (EPA) NOx SIP call, which envisions large reductions in NOx from electric utilities and other point sources. These results are compared to the alternative policy of ton-for-ton NOx emissions trading, similar to that proposed by the EPA for utilities. We find that emission reduction targets can be met at roughly 50% cost savings under a trading program when there are no transaction costs. The paper examines a number of alternative economic incentive policies that have the potential to improve upon the utility NOx trading plan proposed by EPA, including incorporation of other point sources in the trading program, incorporation of ancillary PM benefits to ozone reductions in the trading program, and trading on the basis of ozone exposures that incorporates the spatial impact of emissions on ozone levels. For the latter analysis, we examine spatially differentiated permit systems for reducing ozone exposures under different and uncertain meteorological conditions, including an empirical analysis of the trade-off between the reliability (or degree of certainty) of meeting ozone exposure reduction targets and the cost of NOx control. Finally, several policies that combine costs and health benefits from both ozone and PM reductions are compared to command-and-control and single-pollutant trading policies. The first of these is a full multipollutant trading system that achieves a health benefit goal, with the interpollutant trading ratios governed by the ratio of unit health benefits of ozone and PM. Then, a model that maximizes aggregate benefits from both ozone and PM exposure reductions net of the costs of NOx controls is estimated. EPA’s program appears to be reasonably cost-effective compared to all of the other more complex trading programs we examined. It may even be considered an optimal policy that maximizes net aggregate benefits if the high estimate of benefits is used in which mortality risk is linked to ozone exposure. Without this controversial assumption, however, we find that EPA’s NOx reduction target is far too large.

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

State of the Art in the Optimisation of Wind Turbine Performance Using CFD

Wind energy has received increasing attention in recent years due to its sustainability and geographically wide availability. The efficiency of wind energy utilisation highly depends on the performance of wind turbines, which convert the kinetic energy in wind into electrical energy. In order to optimise wind turbine performance and reduce the cost of next-generation wind turbines, it is crucial to have a view of the state of the art in the key aspects on the performance optimisation of wind turbines using Computational Fluid Dynamics (CFD), which has attracted enormous interest in the development of next-generation wind turbines in recent years. This paper presents a comprehensive review of the state-of-the-art progress on optimisation of wind turbine performance using CFD, reviewing the objective functions to judge the performance of wind turbine, CFD approaches applied in the simulation of wind turbines and optimisation algorithms for wind turbine performance. This paper has been written for both researchers new to this research area by summarising underlying theory whilst presenting a comprehensive review on the up-to-date studies, and experts in the field of study by collecting a comprehensive list of related references where the details of computational methods that have been employed lately can be obtained

A multi-objective genetic algorithm for the design of pressure swing adsorption

Pressure Swing Adsorption (PSA) is a cyclic separation process, more advantageous over other separation options for middle scale processes. Automated tools for the design of PSA processes would be beneficial for the development of the technology, but their development is a difficult task due to the complexity of the simulation of PSA cycles and the computational effort needed to detect the performance at cyclic steady state. We present a preliminary investigation of the performance of a custom multi-objective genetic algorithm (MOGA) for the optimisation of a fast cycle PSA operation, the separation of air for N2 production. The simulation requires a detailed diffusion model, which involves coupled nonlinear partial differential and algebraic equations (PDAEs). The efficiency of MOGA to handle this complex problem has been assessed by comparison with direct search methods. An analysis of the effect of MOGA parameters on the performance is also presented