Preparation of Zinc Cyanide and Elimination of Free Alkali in Cyanide

Zinc cyanide can be prepared by double decomposition reaction of sodium cyanide or potassium cyanide with zinc chloride. Before the preparation of zinc cyanide, it is necessary to remove the free alkali in the raw material sodium cyanide or potassium cyanide. Magnesium chloride is used to remove the free alkali in sodium cyanide or potassium cyanide. The concentration of cyanide ion in potassium cyanide solution under different conditions is measured. The results show that magnesium chloride can not only remove the original free alkali in potassium cyanide, but also promote the hydrolysis of potassium cyanide to produce new free alkali and react with it to form new precipitate. By analyzing the components of the precipitate, it is determined that the main components in the precipitation are Mg(OH)2 and MgCO3. The final conclusion: after the free alkali in cyanide is removed by magnesium chloride, the solution must be quickly put into the double decomposition reaction to reduce the further loss of cyanide ion; In the preparation of zinc cyanide, the adding order needs to be noted: the cyanide aqueous solution must be added into the zinc chloride solution to ensure the excess of zinc chloride.

Latent Variables Analysis in Structural Models: A New Decomposition of the Kalman Smoother

This paper advocates chaining the decomposition of shocks into contributions from forecast errors to the shock decomposition of the latent vector to better understand model inference about latent variables. Such a double decomposition allows us to gauge the inuence of data on latent variables, like the data decomposition. However, by taking into account the transmission mechanisms of each type of shock, we can highlight the economic structure underlying the relationship between the data and the latent variables. We demonstrate the usefulness of this approach by detailing the role of observable variables in estimating the output gap in two models.

Robustness study of the tricalcium phosphate synthesis by using Taguchi’s approach

In this work we propose a contribution for the optimization of the tricalcium phosphate synthesis by a double decomposition with a Ca/P ratio equal to 1.5, using Taguchi’s approach in the methodology of experiment design. We used a model involving four factors, namely the pH of the reaction, the concentration of Ca2+ ions, the temperature and the reaction time. We reduced the number of factors by subtracting the temperature, because it varies randomly in its range of variation. So we have developed a much simpler and more robust model by a double optimization. It consists in finding a configuration of the retained factors to synthesize a product with a Ca/P equal to 1.5 with the minimum of dispersion. We propose a synthesis process insensitive to random variations in temperature in its field of experimental variation defined previously. The synthesis process of the tricalcium phosphate is robust and insensitive on the temperature in the range of variation that was analyzed. The resolution of the mathematical model proposes different ways for this synthesis by a factorial variation of the three remaining factors. The proposed mathematical model is linear and efficient with very satisfactory statistical indicators.

Diagnosis of Ball Bearing Faults Using Double Decomposition Technique

The rolling element bearing is one of the most significant components of any rotating machinery. However, the foremost cause of malfunction in any rotating machine is due to defects like cracks, dents, spall, pits, etc. in ball bearings. Early diagnosis of these bearing faults is highly essential to avoid an accidental shutdown of rotating machinery. In the present work, a novel technique of bearing fault diagnosis is proposed following double decomposition of the vibration activity. The experimentally recorded vibration signals are processed through two stages of decomposition viz. Empirical Mode Decomposition and Tunable Q-factor Wavelet Transform based Time-Frequency decomposition. Subsequently, sub-bands of decomposed time-frequency activity are acquired and discriminable features are computed. Fractal Dimension (FD) based features are extracted from each decomposed sub-band as complexity measures of time-frequency sub-bands. In order to classify bearing faults, a Support Vector Machine classifier is trained with acquired features and classification performance is evaluated. The results of classification reveal that the proposed double decomposition technique is a potential candidate in extracting viable vibration signatures for fault identification. The study is conducted on Case Western Reserve University bearing datasets.