Microbial fuel cells (MFCs) have recently been integrated with membrane bioreactors (MBRs) for wastewater treatment and energy recovery. However, the impact of integration of the two reactors on membrane fouling of MBR has not been reported yet. In this study, MFCs equipped with different external resistances (1-10 000 ohm) were operated, and membrane-fouling potentials of the MFC anode effluents were directly measured to study the impact of anodic respiration by exoelectrogens on membrane fouling. It was found that although the COD removal efficiency was comparable, the fouling potential was significantly reduced due to less production of biopolymer (a major foulant) in MFCs equipped with lower external resistance (i.e., with higher current generation) as compared with aerobic respiration. Furthermore, it was confirmed that Geobacter sulfurreducens strain PCA, a dominant exoelectrogen in anode biofilms of MFCs in this study, produced less biopolymer under anodic respiration condition than fumarate (anaerobic) respiration condition, resulting in lower membrane-fouling potential. Taken together, anodic respiration can mitigate membrane fouling of MBR due to lower biopolymer production, suggesting that development of an electrode-assisted MBR (e-MBR) without aeration is feasible.
Combinatorial optimization problems with a large solution space are difficult to solve just using von Neumann computers. Ising machines or annealing machines have been developed to tackle these problems as a promising Non-von Neumann computer. In order to use these annealing machines, every combinatorial optimization problem is mapped onto the physical Ising model, which consists of spins, interactions between them, and their external magnetic fields. Then the annealing machines operate so as to search the ground state of the physical Ising model, which corresponds to the optimal solution of the original combinatorial optimization problem. A combinatorial optimization problem can be firstly described by an ideal fully-connected Ising model but it is very hard to embed it onto the physical Ising model topology of a particular annealing machine, which causes one of the largest issues in annealing machines. In this paper, we propose a fully-connected Ising model embedding method targeting for CMOS annealing machine. The key idea is that the proposed method replicates every logical spin in a fully-connected Ising model and embeds each logical spin onto the physical spins with the same chain length. Experimental results through an actual combinatorial problem show that the proposed method obtains spin embeddings superior to the conventional de facto standard method, in terms of the embedding time and the probability of obtaining a feasible solution.
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