Environmentally clean materials from contaminated marine dredged sludge, wood ashes and lime production wastes

The article considers the approaches to building a circular economy based on the example of a functioning Russian industrial hub. Among the features analyzed are the specificities of the industrial symbiosis development of chemical enterprises and material flows that link different technical processes, namely the production of soda ash, lime, salt, and gypsum. This study assesses the pollutant emissions from the new technical processes and the use of accumulated chrome production wastes as secondary material resources. Likewise, the article demonstrates the possibility of direct absorption of carbon dioxide, as a byproduct of lime production during soda ash manufacturing. The study ends with recommendations for considering the aspects for the use of secondary resources in determining the sectoral and inter-sectoral best available techniques. Similarly, it sheds light on possibilities for optimizing the system of technological regulation and general binding rules application for managing insignificant environmental aspects of industrial production.

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RHEOLOGICAL PROPERTIES OF CERAMIC MASSES: IMPROVEMENT BY COMPLEX MATERIAL ACTIVATION

The Russian Automobile and Highway Industry Journal ◽

10.26518/2071-7296-2019-3-334-351 ◽

2019 ◽

Vol 16 (3) ◽

pp. 334-351

Author(s):

A. S. Mavlyanov ◽

E. K. Sardarbekova

Keyword(s):

Rheological Properties ◽

Raw Materials ◽

Sensitivity Coefficient ◽

Raw Material ◽

Clay Material ◽

Fibrous Materials ◽

Chemical Production ◽

Production Wastes ◽

Pass Through ◽

The Masses

Introduction. The objective of the research is to study the effect of the complex activation of the alumina raw material on the rheological properties of the ceramic mass. In addition, the authors investigate solutions for the application of optimal coagulation structures based on loams and ash together with plastic certificates.Materials and methods. The authors used the local forest like reserves of clay loams at the BashKarasu, ash fields of the Bishkek Central Heating Centre (BTEC) and plasticizer (sodium naphthenate obtained from alkaline chemical production wastes) as fibrous materials. Moreover, the authors defined technological properties of raw materials within standard laboratory methodology in accordance with current GOSTs.Results. The researchers tested plastic durability on variously prepared masses for the choice of optimal structures. The paper demonstrated the plastic durability of complexly activated compounds comparing with non-activated and mechanically activated compounds. The sensitivity coefficient increased the amount of clay loams by mechanically and complexly activated, which predetermined the possibility of intensifying the process of drying samples based on complexly activated masses.Discussion and conclusions. However, mechanical activation of clay material reduces the period of relaxation and increases the elasticity coefficient of ceramic masses by 1.8–3.4 times, meanwhile decreases elasticity, viscosity and the conventional power during molding, which generally worsens the molding properties of the masses. Сomplex activation of ash-clay material decreases the period of relaxation and provides an increase in elasticity, plasticity of ceramic masses by 46–47%, reduction in viscosity by 1.5–2 times, conventional power on molding by 37–122% in comparison with MA clay loams. Ceramic masses based on spacecraft alumina raw materials belong to the SMT with improved rheological properties; products based on them pass through the mouthpiece for 5–7 seconds.

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SHS PROCESSING OF MINERAL RAW MATERIALS AND PRODUCTION WASTES IN ATMOSPHERIC ENVIRONMENT

СИНТЕЗ И КОНСОЛИДАЦИЯ ПОРОШКОВЫХ МАТЕРИАЛОВ. Сборник тезисов Международной конференции ◽

10.30826/scpm2018013 ◽

2018 ◽

Author(s):

V.E. LORYAN ◽

◽

A.R. KACHIN ◽

I.P. BOROVINSKAYA ◽

◽

...

Keyword(s):

Raw Materials ◽

Atmospheric Environment ◽

Mineral Raw Materials ◽

Production Wastes

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Leachate Impact from Gas and Oil Exploration & Production Wastes in the Marcellus Shale Region

Proceedings of the Water Environment Federation ◽

10.2175/193864715819541990 ◽

2015 ◽

Vol 2015 (10) ◽

pp. 1124-1133 ◽

Cited By ~ 1

Author(s):

Ivan Cooper ◽

Eric Chiado

Keyword(s):

Marcellus Shale ◽

Oil Exploration ◽

Production Wastes

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Application of flocculation combined with vacuum preloading to reduce river-dredged sludge

Marine Georesources and Geotechnology ◽

10.1080/1064119x.2018.1564092 ◽

2019 ◽

Vol 38 (2) ◽

pp. 164-173 ◽

Cited By ~ 7

Author(s):

Feiyu Liu ◽

Wenqing Wu ◽

Hongtao Fu ◽

Jun Wang ◽

Jun Hai ◽

...

Keyword(s):

Vacuum Preloading ◽

Dredged Sludge

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Modeling of lime production process using artificial neural network

Chemical Product and Process Modeling ◽

10.1515/cppm-2021-0032 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Abolghasem Daeichian ◽

Rana Shahramfar ◽

Elham Heidari

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Production Process ◽

High Energy ◽

Ann Model ◽

Artificial Neural ◽

Rotary Kilns ◽

Input Variables ◽

Artificial Neural Network Ann ◽

Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

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