Missouri University of Science and Technology

About: Missouri University of Science and Technology is a education organization based out in Rolla, Missouri, United States. It is known for research contribution in the topics: Artificial neural network & Control theory. The organization has 9380 authors who have published 21161 publications receiving 462544 citations. The organization is also known as: Missouri S&T & University of Missouri–Rolla.

Long-term low DO enriches and shifts nitrifier community in activated sludge.

Abstract: In the activated sludge process, reducing the operational dissolved oxygen (DO) concentration can improve oxygen transfer efficiency, thereby reducing energy use. The low DO, however, may result in incomplete nitrification. This research investigated the long-term effect of low DO on the nitrification performance of activated sludge. Results indicated that, for reactors with 10 and 40 day solids retention times (SRTs), complete nitrification was accomplished after a long-term operation with a DO of 0.37 and 0.16 mg/L, respectively. Under long-term low DO conditions, nitrite oxidizing bacteria (NOB) became a better oxygen competitor than ammonia oxidizing bacteria (AOB) and, as a result, no nitrite accumulated. Real-time PCR assays indicated that the long-term low DO enriched both AOB and NOB in activated sludge, increasing the sludge nitrification capacity and diminishing the adverse effect of low DO on the overall nitrification performance. The increase in the population size of nitrifiers was likely resulted from the reduced nitrifier endogenous decay rate by a low DO. Under long-term low DO conditions, Nitrosomonas europaea/eutropha remained as the dominant AOB, whereas the number of Nitrospira-like NOB became much greater than Nitrobacter-like NOB, especially for the 40 day SRT sludge. The enrichment and shift of the nitrifier community reduced the adverse effect of low DO on nitrification; therefore, low DO operation of a complete nitrification process is feasible.

Neural Network-Based Event-Triggered State Feedback Control of Nonlinear Continuous-Time Systems

Abstract: This paper presents a novel approximation-based event-triggered control of multi-input multi-output uncertain nonlinear continuous-time systems in affine form. The controller is approximated using a linearly parameterized neural network (NN) in the context of event-based sampling. After revisiting the NN approximation property in the context of event-based sampling, an event-triggered condition is proposed using the Lyapunov technique to reduce the network resource utilization and to generate the required number of events for the NN approximation. In addition, a novel weight update law for aperiodic tuning of the NN weights at triggered instants is proposed to relax the knowledge of complete system dynamics and to reduce the computation when compared with the traditional NN-based control. Nonetheless, a nonzero positive lower bound for the inter-event times is guaranteed to avoid the accumulation of events or Zeno behavior. For analyzing the stability, the event-triggered system is modeled as a nonlinear impulsive dynamical system and the Lyapunov technique is used to show local ultimate boundedness of all signals. Furthermore, in order to overcome the unnecessary triggered events when the system states are inside the ultimate bound, a dead-zone operator is used to reset the event-trigger errors to zero. Finally, the analytical design is substantiated with numerical results.

A neural network thrust force controller to minimize delamination during drilling of graphite-epoxy laminates

Abstract: Delamination is a well-recognized problem associated with drilling fiber-reinforced composite materials (FRCMs). The most noted problems occur as the drill enters and exits the FRCM. Since drilling is often a final operation during assembly, any defects introduced in parts through the drilling process that result in the part being rejected represent an expensive loss. Studies based on linear-elastic fracture mechanics theory have proposed critical cutting and thrust forces in the various drilling regions that can be used as a guide in preventing crack growth or delamination. Using these critical force curves as a guide, a thrust force controller was developed to minimize the delamination while drilling a graphite-epoxy laminate. A neural network control scheme was implemented which required a neural network identifier to model the drilling dynamics and a neural network controller to learn the relationship between feed rate and the desired thrust force. Experimental results verifying the validity of this control approach as well as the robustness of the design are presented. Visual measurements of the delamination zones were used to quantify the benefits of the thrust force controlled drilling process versus the conventional constant feed rate drilling process.