An improved Bayesian-based wavelet package denoising method for data reconciliation to coking chemical process

Measurement data usually do not reflect actual chemical processes correctly because of inevitable errors in measurement, which is known as unbalance of measurement data. Data reconciliation and gross error detection are methods of processing measuring instruments that are inconsistent with mass and energy balances. At present, research on data reconciliation mainly focuses on the steady state model of the linear system. However, data reconciliation cannot be implemented in cases of sudden changes in production conditions, operations or instability, or discontinuous data acquisition. An improved Bayesian based on wavelet package denoising for data reconciliation is proposed. After a measurement signal is decomposed into a certain level, the optimal tree is selected with Bayesian classification. Bayesian classification was used to select the node of the optimal tree that would accelerate the calculation speed. The simulation and the coking chemical application show that the improved denoising method contributes to the effectiveness and efficiency of data reconciliation

Discovery of a Novel Class of Bicyclo[3.1.0]hexanylpiperazines as Noncompetitive Neuropeptide Y Y1 Antagonists

A novel class of bicyclo[3.1.0]­hexanylpiperazine neuropeptide Y (NPY) Y1 antagonists has been designed and synthesized. Scatchard binding analysis showed these compounds to be noncompetitive with [¹²⁵I]­PYY binding to the Y1 receptor. The most potent member, 1-((1α,3α,5α,6β)-6-(3-ethoxyphenyl)-3-methylbicyclo[3.1.0]­hexan-6-yl)-4-phenylpiperazine (<b>2</b>) had an IC₅₀ = 62 nM and displayed excellent oral bioavailability in rat (% <i>F</i> po = 80), as well as good brain penetration (B/P ratio = 0.61). In a spontaneous nocturnal feeding study with male Sprague–Dawley rats, <b>2</b> significantly reduced food intake during a 12 h period