The application of artificial intelligence techniques for performance optimization of the fuel lean gas reburn (FLGR) system is investigated. A multilayer, feedforward artificial neural network is applied to model static nonlinear relationships between the distribution of injected natural gas into the upper region of the furnace of a coal-fired boiler and the corresponding oxides of nitrogen (NOx) emissions exiting the furnace. Based on this model, optimal distributions of injected gas are determined such that the largest NOx reduction is achieved for each value of total injected gas. This optimization is accomplished through the development of a new optimization method based on neural networks. This new optimal control algorithm, which can be used as an alternative generic tool for solving multidimensional nonlinear constrained optimization problems, is described and its results are successfully validated against an off-the-shelf tool for solving mathematical programming problems. Encouraging results obtained using plant data from one of Commonwealth Edison's coal-fired electric power plants demonstrate the feasibility of the overall approach. Preliminary results show that the use of this intelligent controller will also enable the determination of the most cost-effective operating conditions of the FLGR system by considering, along with the optimal distribution of the injected gas, the cost differential between natural gas and coal and the open-market price of NOx emission credits. Further study, however, is necessary, including the construction of a more comprehensive database, needed to develop high-fidelity process models and to add carbon monoxide (CO) emissions to the model of the gas reburn system. 相似文献
Environmental Science and Pollution Research - Ionizing radiation (IR) is a form of high energy. It poses a serious threat to organisms, but radiotherapy is a key therapeutic strategy for various... 相似文献
Environmental Science and Pollution Research - Magnetic nanostructured MnFe2O4 with different morphologies, synthesized via chemical co-precipitation and hydrothermal method, was assayed as... 相似文献
Environmental Science and Pollution Research - The pollution of heavy metals (HMs) in the soil has become one of the important factors affecting the national environment and human health.... 相似文献
Microbial communities are important for high composting efficiency and good quality composts. This study was conducted to compare the changes of physicochemical and bacterial characteristics in composting from different raw materials, including chicken manure (CM), duck manure (DM), sheep manure (SM), food waste (FW), and vegetable waste (VW). The role and interactions of core bacteria and their contribution to maturity in diverse composts were analyzed by advanced bioinformatics methods combined sequencing with co-occurrence network and structural equation modeling (SEM). Results indicated that there were obviously different bacterial composition and diversity in composting from diverse sources. FW had a low pH and different physiochemical characteristics compared to other composts but they all achieved similar maturity products. Redundancy analysis suggested total organic carbon, phosphorus, and temperature governed the composition of microbial species but key factors were different in diverse composts. Network analysis showed completely different interactions of core bacterial community from diverse composts but Thermobifida was the ubiquitous core bacteria in composting bacterial network. Sphaerobacter and Lactobacillus as core genus were presented in the starting mesophilic and thermophilic phases of composting from manure (CM, DM, SM) and municipal solid waste (FW, VW), respectively. SEM indicated core bacteria had the positive, direct, and the biggest (>?80%) effects on composting maturity. Therefore, this study presents theoretical basis to identify and enhance the core bacteria for improving full-scale composting efficiency facing more and more organic wastes.
