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Coal water slurry is an advanced and efficient coal utilization technology, prepare coal water slurry by gasification wastewater can realize the recycling of wastewater and improve the combustion or gasification efficiency of the slurry. Stabilizer is an important component to ensure fluidity and stability in industrial applications of coal water slurry, and the complex substances in wastewater for coal water slurry preparation have a great influence on the stabilizer. Therefore, in this paper, the influence of the major substances in gasification wastewater on the stabilizer is studied. Results show that (a) Ammonia nitrogen substances have adverse effects on stabilizers, because the ionized NH4+ in the solution will bond with the polar groups on the stabilizer, which will affect the stabilizer to lose its effectiveness and reduce the stability of the slurry. (b) Small organic molecules have little effect on stabilizers, this is because the binding strength and quantity of stabilizer to water molecules is much larger than that of organic matters such as phenolic substances. (c) Highvalent metal ions have a greater impact on stabilizers, the metal ions easily form gels or precipitates with the stabilizer, causing it to lose its effectiveness, and also have an electrostatic effect with the carboxyl group on the side chain of the stabilizer, which makes the stabilizer easy to aggregate together and lose the steric hindrance effect, finally cause the stability of the slurry reduced. The conclusion of the influence of substances in wastewater on stabilizer can provide a technical reference for the selection of suitable wastewater and stabilizer to prepare coal water slurry, then promoting the efficient use of energy.

The coal gasification process produces a large amount of wastewater which is seriously polluted and difficult to biochemically treat. The regasification of coal water slurry produced from gasification wastewater meets the requirements of clean and efficient use of energy and the concept of circular economy. In this paper, the slurryability of coal water slurry prepared with gasification wastewater was measured, and the influence mechanism of organic matter, metal ions, and ammonia nitrogen components in coal gasification wastewater on slurryability has been studied. Results show that (a) Coal water slurry can be prepared with gasification wastewater, and the composition of wastewater has a great influence on the slurryability. (b) Phenols and alcohols in wastewater are not conducive to slurryability, while urethane in wastewater is beneficial for slurrying. (c) K+ and Na+ in wastewater have little effect on the slurryablity even in high concentration, while Mg2+ and Ca2+ have basically no effect on the slurryability under the concentration range of coal gasification wastewater. However, Fe3+ has a negative effect and Cu3+ has a positive effect on the slurryability at low concentrations. (d) Ammonia nitrogen can affect the slurryability of coal water slurry by affecting the pH of the solution. NH4OH solution is alkaline, which is conducive to slurrying, while (NH4)2SO4 and NH4Cl solutions are acidic, resulting in poor slurryability of coal water slurry. [ABSTRACT FROM AUTHOR]

A variety of wastewaters can be generated in the coal chemical industry, and their treatment processes are complicated and have difficulty meeting standards. Using wastewater to prepare coal water slurry is an efficient and convenient new approach. The concentration of coal wastewater slurry is related to the content of the main wastewater components. A backpropagation neural network is developed to predict the maximum slurry concentration and analyze the mechanism at the data level according to the main component indicators, and a particle swarm algorithm is used to improve the neural network. The results are as follows: (a) it is feasible to predict the maximum concentration of coal wastewater slurry by a neural network, and a particle swarm algorithm can effectively improve the prediction accuracy in different models, reducing mean absolute error by up to 0.44%; (b) different input factors have different impacts on model prediction results—organic matter, ammonia nitrogen, and monovalent metal ions content as input factors to predict the maximum slurry concentration can get the most accurate results, obtaining a mean absolute error of 0.16% for the optimized backpropagation neural network and the lowest mean square error; and (c) divalent metal ions and phenols content are not suitable as input factors for predicting, as they all cause an increase in model error due to their weak or complex effects on the slurryability. [ABSTRACT FROM AUTHOR]

Zinc metal is recognized as the most promising anode for aqueous energy storage but suffers from severe dendrite growth and poor reversibility. However, the coulombic efficiency lacks specificity for zinc dendrite growth, particularly in Zn||Zn symmetric cells. Herein, a novel indicator (f D ) based on the characteristic crystallization peaks is proposed to evaluate the growth and distribution of zinc dendrites. As a proof of concept, triethylenetetramine (TETA) is adopted as an electrolyte additive to manipulate the zinc flux for uniform deposition, with a corroborating low f D value. A highly durable zinc symmetric cell is achieved, lasting over 2500 h at 10 mA cm -2 and 400 h at a large discharge of depth (10 mA cm -2 , 10 mAh cm -2 ). Supported by the low f D value, the Zn||TETA-ZnSO 4 ||MnO 2 batteries overcome the sudden short circuit and fast capacity fading. The study provides a feasible method to evaluate zinc dendrites and sheds light on the design of highly reversible zinc anodes., (© 2024 Wiley‐VCH GmbH.)