Neural network approach to predicting mercury emission from utility boiler

The feasibility of using an ANN method to predict the mercury emission and speciation in the flue gas of a power station under un-tested combustion/operational conditions is evaluated. Based on existing field testing datasets for the emissions of three utility boilers, a 3-layer back-propagation network is applied to predict the mercury speciation at the stack. The whole prediction procedure includes： collection of data, structuring an artificial neural network （ANN） model, training process and error evaluation. A total of 59 parameters of coal and ash analyses and power plant operating conditions are treated as input variables, and the actual mercury emissions and their speciation data are used to supervise the training process and verify the performance of prediction modeling. The precision of model prediction （ root- mean-square error is 0. 8 μg/Nm3 for elemental mercury and 0. 9 μg/Nm3 for total mercury） is acceptable since the spikes of semi- mercury continuous emission monitor （SCEM） with wet conversion modules are taken into consideration.

Powering Renewable Programs: The Utility Perspective

In order to make renewable energy projects successful, there are many factors that utilities need to consider. These include policy drivers, assessing what renewable technologies it will employ, identifying the rates and pricing incentives that could be made available, and how customers can be better engaged. Utilities have created renewable programs with varying degrees of customer participation: some have taken the initiative to provide customers with 100% renewable generated power, others rely exclusively on customers to participate to meet renewable energy goals and the last alternative is a blend of both in which utilities offer customers the option to purchase renewable power matches or install and generate their own renewable power. Overall, the economics of solar and wind technologies are trending in the upward direction—the costs of the technologies are decreasing and the yields are getting higher. Better still, predictive modeling, energy storage and the plethora of research in this area will only make the prospects of integrating renewables more viable.

An Optimized Oxygen System Scheduling With Electricity Cost Consideration in Steel Industry

As an essential energy resource in steel industry, oxygen is widely utilized in many production procedures. With different demands of the oxygen amount, a gap between the generation and consumption always occurs. Therefore, its related optimization and scheduling work along with the electricity cost to fill the gap has a great impact on daily production and efficient energy utilization in steel plant. Considering an oxygen system in a steel plant in China, a nonlinear programming model for oxygen system scheduling is proposed in this study, which concerns not only the practical characteristics of the energy pipeline network, but also the electricity cost acquired by a fitting regression modeling between the load of air separation units U+0028 ASU U+0029 and its corresponding electricity consumption. A set of constraints is formulated for restricting the practical adjusting capacity and filling the imbalance gap of oxygen. To solve the proposed scheduling model with electricity cost consideration, a particle swarm optimization U+0028 PSO U+0029 algorithm is then adopted. To verify the effectiveness of the proposed approach, a large number of experiments employing the real data from this plant are carried out, both for the fitting regression and the scheduling optimization phases. And the results demonstrate that such a practice-based solution successfully resolves the oxygen scheduling problem and simultaneously minimizes the electricity cost, which will be beneficial for the enterprise. © 2017 Chinese Association of Automation.

Transformation of mercury speciation through the SCR system in power plants

Coal-fired utility boilers are now identified as the largest source of mercury in the United States. There is speculation that the installation of selective catalytic reduction （SCR） system for reduction of NOx can also prompt the oxidation and removal of mercury. In this paper, tests at six full-scale power plants with similar type of the SCR systems are conducted to investigate the effect of the SCR on the transformation of mercury speciation. The results show that the SCR system can achieve more than 70%-80% oxidation of elemental mercury and enhance the mercury removal ability in these units. The oxidation of elemental mercury in the SCR system strongly depends on the coal properties and the operation conditions of the SCR systems. The content of chloride in the coal is the key factor for the oxidization process and the maximum oxidation of elemental mercury is found when chloride content changes from 400 to 600 ppm. The sulfur content is no significant impact on oxidation of elemental mercury.

Separation and comprehensive utilization of valuable elements in Ti-bearing electric arc furnace molten slag

A novel route to comprehensive utilization of valuable elements such as Ti, A1, Si and Mg in Ti-bearing electric arc furnace molten slag （Ti-bearing EAF slag） was proposed. The route can be expressed as a three-step process including alkali fusion, water leaching and acidolysis. Following these processes under the optimum conditions, the recovery ratios of TiO2, Al2O3, SiO2 and MgO were about 97.5, 93.5, 27.9 and 53.5%, respectively. Meanwhile, nanostructured TiO2, NaA zeolite and Mg（OH）2 fire retardant were synthesized simultaneously by using Ti-bearing EAF slag as raw materials. In addition, the photocatalytic activity of prepared nanostructured TiO2 and the adsorption property of obtained NaA zeolite were investigated. The results showed that the photodegradation efficiency of as-prepared TiO2 was 80% for rhodamine B and the adsorption efficiency of NaA zeolite was 61% for Cu2＋ under the optimum conditions.

Theoretical analysis and experimental verification of a cost-effective chromatic dispersion monitoring method in a 40-Gb/s optical fiber communication system

A cost-effective technique for in-service chromatic dispersion monitoring in a 40-Gb/s optical communication system is proposed. Microwave devices are adopted to detect the electrical power of a specific frequency band. A simplified theoretical model is proposed and discussed focusing on the relationship between electrical power and chromatic dispersion at different frequency bands. The dynamic monitoring of chromatic dispersion is achieved using devices such as PIN detector, microwave amplifier, narrow-band microwave filter, and electrical power detector. The maximum detectable chromatic dispersion is 130 ps/nm and a resolution of 5.2 ps/nm/dB has been achieved in the frequency band centered at 12 GHz.

Non-blocking four-port optical router based on thermooptic silicon microrings

By using silicon-on-insulator(SOI) platform, 12 channel waveguides, and four parallel-coupling one-microring resonator routing elements, a non-blocking four-port optical router is proposed. Structure design and optimization are performed on the routing elements at 1 550 nm. At drop state with a power consumption of 0 m W, the insertion loss of the drop port is less than 1.12 d B, and the crosstalk between the two output ports is less than-28 d B; at through state with a power consumption of 22 m W, the insertion loss of the through port is less than 0.45 d B, and the crosstalk between the two output ports is below-21 d B. Routing topology and function are demonstrated for the four-port optical router. The router can work at nine non-blocking routing states using the thermo-optic(TO) effect of silicon for tuning the resonance of each switching element. Detailed characterizations are presented, including output spectrum, insertion loss, and crosstalk. According to the analysis on all the data links of the router, the insertion loss is within the range of 0.13—3.36 d B, and the crosstalk is less than-19.46 d B. The router can meet the need of large-scale optical network-on-chip(ONo C).

Technical Characteristics Analysis for Toyota Prius Hybrid Vehicle

In order to solve the problem effectively that increasing car ownership relies on limited resources like oil and the resulting environmental pollution because of vehicle exhaust,＂New energy vehicles＂is not a new conception which is mentioned frequently and has been used in practical applications in our daily life. As a typical representative of the use of new energy vehicles,Prius,a kind of gas-electric hybrid cars which are made by Toyota is a very typical type in technique and application. Using Prius as an example,we will focus on its chemical reaction principle of electrical energy storage device and evaluation parameters,the structure of driving motors and gives the dqmodel of the electrical machine in dqcoordinates,the structure of dynamic coupling device,convertor assembly which including four kinds of convertor and its function,finally,introducing the working principles and characteristics of the system assembly in this paper.