This
paper aims to demonstrate an industrial retrofit of NOx reduction
through use of a new technology, high-temperature selective noncatalytic
reduction (abbr. HTSNCR), in a 50 MWe tangentially firing boiler of
pulverized coal. Over the last several years, the characteristics
of HTSNCR were investigated in the bench-scale experiments, as presenting
the temperature window of SNCR can extend to a higher temperature
range in the absence of oxygen. Therefore, HTSNCR provides a promising
option of greater NOx reduction via injecting urea solution or ammonia
into the primary combustion zone specialized with oxygen of nearly
zero in furnace. These retrofit experiments in this paper successfully
showed the ability of HTSNCR. NOx emission of 68 mg/Nm3 was finally achieved through use of the optimum hybrid application
of HTSNCR, SNCR, and OFA. The overall reduction efficiency is approximately
90%, in which 17% is devoted by HTSNCR. The main factors in HTSNCR
were studied extensively, including (1) primary stoichiometric ratio
(SR1) of air staging, (2) normalized stoichiometric ratio
(NSR) of reagent quantity injected, (3) allocation of injectors, e.g.,
on the corners or at the middle of side walls, (4) ammonia slip brought
about by HTSNCR or SNCR, (5) optimized hybrid configuration about
SNCR and HTSNCR. On the basis of the optimum setting of the above
factors, two key features of HTSNCR employed in a tangentially firing
furnace were obtained. First, there is a critical minimum value of
NOx emission in the relationship of NOx emission versus NSR1 of HTSNCR. More NSR1 beyond the critical value, i.e.,
more reagent quantity injected, results in more NOx formation. Secondarily,
the injection of reagent near the corners is beneficial to reach higher
NOx reduction rather than that injected from the side walls, due to
the aerodynamics in the tangentially firing furnace.
In this paper, the synchronization problem for a class of generalized neural networks with time-varying delays and reaction-diffusion terms is investigated concerning Neumann boundary conditions in terms of p-norm. The proposed generalized neural networks model includes reaction-diffusion local field neural networks and reaction-diffusion static neural networks as its special cases. By establishing a new inequality, some simple and useful conditions are obtained analytically to guarantee the global exponential synchronization of the addressed neural networks under the periodically intermittent control. According to the theoretical results, the influences of diffusion coefficients, diffusion space, and control rate on synchronization are analyzed. Finally, the feasibility and effectiveness of the proposed methods are shown by simulation examples, and by choosing different diffusion coefficients, diffusion spaces, and control rates, different controlled synchronization states can be obtained.
Aiming at the problem of impact angle constraint and input saturation, an integrated guidance and control (IGC) algorithm with impact angle constraint and input saturation is proposed. A three-channel independent design model of missile IGC with impact angle constraint is established, and an extended state observer with fast finite-time convergence is designed to estimate and compensate model errors and coupling relationship between channels. Based on the nonsingular terminal sliding mode control and backstepping control, the IGC three-channel independent design is completed. Nussbaum function and an auxiliary system are introduced to deal with the input saturation. The Lyapunov function is constructed to prove the finite-time convergence of the IGC algorithm. The missile six-degree-of-freedom simulation results show the effectiveness and superiority of the IGC algorithm.
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