Fast Determination of Vitamin B₂Based on Molecularly Imprinted Electrochemical Sensor

Under the condition of weak acidity of pH 5.2, a sensitive vitamin B2 electrochemical sensor based on molecularly imprinted nonconducting polymer of o-aminophenol by potentiostatic polymerization in the presence of template(vitamin B2) on a glassy carbon electrode was prepared, and its performance was studied. The sensor exhibited good sensitivity and selectivity to VB2. The detection limit went down to 2.3851nM, and a linear relationship between the current incremental and the concentration was found in the range of 10~120nM. And the sensor could use in detection of VB2 real sample for a long time and show good reproducibility. The average recovery rate to VB2 was 98.41%.

An Amperometric Sensor for Sunset Yellow FCF Detection Based on Molecularly Imprinted Polypyrrole

An electrochemical method for fast detecting the concentration of sunset yellow FCF in wine samples was developed in this study. The sensor based on imprinted films which fabricated by electropolymerization of pyrrole on a glassy carbon electrode in the presence of sunset yellow FCF as the template. Comparing to the polypyrrole non-imprinted modified (NIP) electrode, the polypyrrole molecularly imprinted polymer (MIP) electrode improved the electrochemical performance of the sensor significantly. The peak current at about 0.26 V was linear with the concentration of sunset yellow FCF from 0.4 to 2 μM and from 2 to 8 μM. It can be used for more than 10 times to maintain a stable response result. The sensor had the good selectivity on sunset yellow FCF, amaranth and tartrazine, which the selection factors were 1.00, 0.80 and 0.85,respectively.

Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control

In artificial intelligence(AI)based-complex power system management and control technology,one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence evolution.However,there is,currently,nearly no standard technical framework for objective and quantitative intelligence evaluation.In this article,based on a parallel system framework,a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems,by resorting to human intelligence evaluation theories.On this basis,this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning(AutoRL)systems.A parallel system based quantitative assessment and self-evolution(PLASE)system for power grid corrective control AI is thereby constructed,taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment results.Experiment results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent,and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results,effectively,as well as intuitively improving its intelligence level through selfevolution.

Active Power Correction Strategies Based on Deep Reinforcement Learning Part I:A Simulation-driven Solution for Robustness

Employing the novel Deep Reinforcement Learning approach,this paper addresses the active power corrective control in modern power systems.Seeking to minimize the joint effect engendered by operation cost and blackout penalty,this correction strategy focuses on evaluating the robustness and adaptability aspects of the control agent.In Part I of this paper,where robustness is the primary focus,the agent is developed to handle unexpected incidents and guide the stable operation of power grids A Simulation-driven Graph Attention Reinforcement Learning method is proposed to perform robust active power corrective control.The aim of the graph attention networks is to determine the representation of power system states considering the topological features.Monte Carlo tree search is adopted to select the best suitable action set out of the large action space,including generator redispatch and topology control actions.Finally,driven by simulation,a guided training mechanism along with a long-short-term action deployment strategy are designed to help the agent better evaluate the action set while training and to operate more stably when deployed.The efficacy of the proposed method has been demonstrated in the“2020 I earning to Run a Power Network.Neurips Track 1”global competition and the associated cases.Part II of this paper deals with the adaptability case,where the agent is equipped to better adapt to a grid that has an increasing share of renewable energies through the years.