No abstract

A heterotrophic nitrification and aerobic denitrification bacterium, strain D51, was identified as Arthrobacter nicotianae based on morphological, phospholipid fatty acids (PLFAs), and 16S rRNA gene sequence analyses. Further tests demonstrated that strain D51 had the capability to use nitrite, nitrate, or ammonium as the sole nitrogen source in the presence of Cu2+. The maximum removal efficiencies of nitrite, nitrate and ammonium were 68.97%, 78.32%, and 98.70%, respectively. Additionally, the maximum growth rate and denitrification capacity of this strain occurred in the presence of 0.05 mg·L−1 of Cu2+.However, the growth and aerobic denitrification capacity were intensively inhibited by Cu2+ at ≥0.1 mg·L−1. Moreover, gas chromatography indicated that a portion of the nitrogen was transformed into N2O when the nitrite, nitrate, and ammonium were separately used as the sole nitrogen source. This is the first study of the nitrification and denitrification ability of Arthrobacter nicotianae under aerobic conditions, and the first experiment to investigate the impact of Cu2+ concentration on the growth and denitrification ability of this bacteria. The results presented herein extend the known varieties of heterotrophic nitrifying–aerobic denitrifying bacteria and provide useful information regarding the specific bacteria for nitrogen bioremediation of industrial wastewater containing Cu2+.

This paper investigates the station-keeping control of autonomous and remotely-operated vehicles (ARVs) for free-floating manipulation under model uncertainties and external disturbances. A modified adaptive generalized super-twisting algorithm (AGSTA) enhanced by adaptive tracking differentiator (ATD) and reduced-order extended state observer (RESO) is proposed. The ATD is used to obtain the smooth reference signal and its derivative. The RESO is used to estimate and compensate for the model uncertainties and external disturbances in real-time, which enhances the robustness of the controller. The modified AGSTA ensures the fast convergence of the system states and maintains them in a predefined neighborhood of origin without overestimating control gains. Besides, the proposed new variable gain strategy completely avoids the control gains vibrating near the set minimum value. Thanks to the RESO, the proposed controller is model-free and can be easily implemented in practice. The stability of the closed-loop system is analyzed based on Lyapunov’s direct method in the time domain. Finally, the proposed control scheme is applied to the station-keeping control of Haidou-1 ARV, and the simulation results confirm the superiority of the proposed control scheme over the original AGSTA.