Reactivity of ammonium chloride/mercuric chloride mixtures with monel containers. The new compounds (NH4)(2)(NH3)(x)[Ni(NH3)(2)Cl-4] and (NH4)(5)Cl-2[CuCl2][CuCl4]

Ammonium chloride/mercuric chloride mixtures (molar ratio 2: 1) react at 350degreesC with Monel (Cu68Ni32) to yield (NH4)NiCl3 and mercury and copper amalgam, respectively. With larger amounts of (NH4)Cl in the reaction mixture, dark green (NH4)(2)(NH3)(x)[Ni(NH3)(2)Cl-4] (x approximate to 0.77) (1) is also formed as a main product. Light blue crystals of the mixed-valent copper(I,II) chloride (NH4)(5)Cl-5[CuCl2][CuCl4] (2) were obtained as a minor byproduct from a 4:1 reaction mixture. The crystal structures were determined from single crystal X-ray data; (1): tetragonal, I4/mmm, a = 770.9(1), e = 794.2(2) pm, 190 reflections, R-1 = 0.0263; (2): tetragonal, I4/mcm, a = 874.8(1), c = 2329.2(3) pm, 451 reflections, R-1 = 0.0736. In (1) Ni2+ resides in trans-[Ni(NH3)(2)Cl-4](2-) octahedra, and in (2) copper(l) is linearly two-coordinated in ECUC121- and copper(II) resides in a flattened tetrahedron [CuCl4](2-) with a tetrahedricity of 89%. (C) 2001 Elsevier Science.

Ammonium mercury(II) dichloride nitrate, (NH4)(2)HgCl2(NO3)(2)

The title compound, (NH4)(2)HgCl2 (NO3)(2), is a double salt of HgCl2 and NH4NO3 and can also be written as `HgCl2.2NH(4)NO(3)'. The structure contains HgCl2 units which are connected by nitrate groups, through long links of ca. 2.90 Angstrom, to give chains running along [010]. All atoms apart from the two oxygen atoms are located on a mirror plane perpendicular to the b axis. The coordination around mercury is a distorted hexagonal bipyramid.

COD and NH4-N Estimation in the Inflow of Wastewater Treatment Plants using Machine Learning Techniques

The in-line measurement of COD and NH4-N in the WWTP inflow is crucial for the timely monitoring of biological wastewater treatment processes and for the development of advanced control strategies for optimized WWTP operation. As a direct measurement of COD and NH4-N requires expensive and high maintenance in-line probes or analyzers, an approach estimating COD and NH4-N based on standard and spectroscopic in-line inflow measurement systems using Machine Learning Techniques is presented in this paper. The results show that COD estimation using Radom Forest Regression with a normalized MSE of 0.3, which is sufficiently accurate for practical applications, can be achieved using only standard in-line measurements. In the case of NH4-N, a good estimation using Partial Least Squares Regression with a normalized MSE of 0.16 is only possible based on a combination of standard and spectroscopic in-line measurements. Furthermore, the comparison of regression and classification methods shows that both methods perform equally well in most cases.

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