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As a great amount of chemicals are employed to carry out the coating process of paper, the wastewater from paper mill making coated products is characterized by high levels of chemical oxygen demand (COD), colour, and total suspended solids (TSS). In this study, wastewater from a paper mill making coated products was treated by a sequencing batch biofilm reactor (SBBR) after a lab-scale coagulation, resulting in COD, colour, and TSS removal efficiencies of 87.7 ± 1.0%, 33.5 ± 5.2%, and 41.4 ± 3.7%, respectively, which exceeded the biological treatment performance in the paper mill. The removal of COD and colour was attributed to the removal of recalcitrant organic matter, and the removal of TSS was attributed to the biofilm.

Reinforcement Learning, a common framework for AI planing or decision making, is regarded as an effective framework fro planning students’ learning sequences. However, previous research efforts rely on simulation data or students and few of them have verified effectiveness of their algorithms with teaching students in real educational environments. Also, the previous research is hard to using in real task such as online course or real classroom because of the strong assumptions and restrictions. In this paper, We propose a new deep reinforcement learning framework for instructional sequencing that can recommend students with personalized learning exercises in both MOOCs and classrooms. Based on our ATC model, is used to trace students learning process and Deep Reinforcement Learning Agent is used to induce efficient l earning sequences for students adaptively. Both simulation and experiment in classrooms confirm the effectiveness of our method.

In this study, the completely autotrophic nitrogen removal over nitrite (CANON) process was initiated in a sequencing batch biofilm reactor (SBBR). Then the reactor was operated under different IC/N ratios. The total inorganic nitrogen removal efficiency (TINRE) at IC/N ratios of 0.75, 1.0, 1.25, 1.5 and 2.0 were 37.0 ± 11.0%, 58.9 ± 10.2%, 73.9 ± 3.2%, 73.6 ± 1.8% and 72.6 ± 2.0%, respectively. The suitable range of IC/N ratio in this research is 1.25–2.0. The poor nitrogen removal performance at IC/N ratio of 0.75 was due to the lack of growth substrate for AnAOB and low pH simultaneously; at IC/N ratio of 1.0 this was because the substrate concentration was insufficient for fully recovering the AnAOB activities. Microbial analysis indicated that Nitrosomonas, Nitrospira and Candidatus Brocadia were the main ammonium oxidation bacteria (AOB), nitrite oxidation bacteria (NOB) and anammox bacteria (AnAOB), respectively. In addition, at IC ratios of 1.25 or higher, denitrification was promoted with the rise of IC/N ratio, which might be because the change of IC concentrations caused cell lysis of microorganisms and provided organic matter for denitrification.