Pressure swing adsorption (PSA) is a suitable method to enrich the concentration of methane in coal mine methane (CMM) separation processes. However, traditional PSA processes for low-concentration CMM can easily increase the concentration of methane to explosive limits, which subsequently increases the risk of explosion. In this paper, a novel PSA process for low-concentration CMM was studied. Using a mixture of activated carbon (AC) and carbon molecular sieve (CMS) as the adsorbent, this method ensures that the gaseous mixture adsorbed does not reach the explosive limits by adsorbing methane and part of the oxygen simultaneously. Using a two-bed vacuum PSA experimental apparatus, the low-concentration CMM was safely enriched from 20% to more than 30%, with CMS and AC mass ratio of 3.4. The results of experimental studies indicate that the mixture adsorbent (AC and CMS) has explosion-suppression and flameproof characteristics. The igniting resource (methane) will not explode even if it appears in the adsorbent layer and, in addition, the explosion will not propagate through the adsorbent layer if it happens in other areas.
Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO) is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm.
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