Influence of Bio-Coal Properties on Carbonization and Bio-Coke Reactivity

Coke corresponds to 2/3–3/4 of the reducing agents in BF, and by the partial replacement of coking coals with 5–10% of bio-coal, the fossil CO2 emissions from the BF can be lowered by ~4–8%. Coking coal blends with 5% and 10% additions of bio-coals (pre-treated biomass) of different origins and pre-treatment degrees were carbonized at laboratory scale and with a 5% bio-coal addition at technical scale, aiming to understand the impact on the bio-coal properties (ash amount and composition, volatile matter content) and the addition of bio-coke reactivity. A thermogravimetric analyzer (TGA) connected to a quadrupole mass spectroscope monitored the residual mass and off-gases during carbonization. To explore the effect of bio-coal addition on plasticity, optical dilatometer tests were conducted for coking coal blends with 5% and 10% bio-coal addition. The plasticity was lowered with increasing bio-coal addition, but pyrolyzed biomass had a less negative effect on the plasticity compared to torrefied biomasses with a high content of oxygen. The temperature for starting the gasification of coke was in general lowered to a greater extent for bio-cokes produced from coking coal blends containing bio-coals with higher contents of catalyzing oxides. There was no significant difference in the properties of laboratory and technical scale produced coke, in terms of reactivity as measured by TGA. Bio-coke produced with 5% of high temperature torrefied pelletized biomass showed a similar coke strength as reference coke after reaction.

Application Study of Sigmoid Regularization Method in Coke Quality Prediction

Coke is an indispensable and vital flue for blast furnace smelting, during which it plays a key role as a reducing agent, heat source, and support skeleton. Models of prediction of coke quality based on ANN are established to map the functional relationship between quality parameters Mt, Ad, Vdaf, St,d, and caking property (X, Y, and G) of mixed coal and quality parameters Ad, St,d, coke reactivity index (CRI), and coke strength after reaction (CSR) of coke. A regularized network training method based on Sigmoid function is designed considering that redundancy of network structure may lead to the learning of undesired noise, in which weights having little impact on performance and leading to overfitting are removed in terms of computational complexity and training errors. The cascade forward neural network with validation is found to be the most suitable one for coke quality prediction, with errors around 5%, followed by feedforward neural network structure and radial basis neural networks. The cascade forward neural network may play a guiding role during the coke production.

PENGARUH KOMPOSISI CAMPURAN BATU BARA LOKAL DAN IMPOR TERHADAP KUALITAS KOKAS PT ABC

Kokas merupakan hasil karbonisasi batu bara tanpa kontak udara yang menyebabkan berkurangnya kandungan zat terbang pada batu bara sehingga nilai karbon tertambatnya menjadi meningkat. Kokas digunakan sebagai bahan bakar dan bahan pereduksi besi dalam proses pembuatan baja dalam teknologi blast furnace. Kokas yang digunakan harus memenuhi parameter kimia dan fisik yang dipersyaratkan karena memiliki peranan penting dalam meningkatkan produktifitas pembuatan baja. Parameter kimia yang dibutuhkan pada kokas yaitu nilai kadar air, kadar zat terbang, kadar abu, kadar karbon tertambat, sedangkan untuk pengujian kualitas kokas didasarkan pada dua indek degradasi yaitu CRI (coke reaction index) dan CSR (coke strength after reaction). Tidak semua batu bara lokal memiliki kualitas yang memenuhi parameter yang dipersyaratkan, seperti batu bara Adaro dan Marunda Graha Mineral (MGM) dari Kalimantan yang memiliki kualitas dibawah standar yang dipersyaratkan. Penelitian ini bertujuan untuk mendapatkan batu bara sesuai dengan standar yang dipersyaratkan PT ABC melalui pencampuran atau blending batu bara lokal dengan batu bara impor German Creek 8 (GC-8) dan Moranbah North (MN) yang memiliki kualitas yang lebih bagus. Proses karbonisasi dilakukan pada suhu 1000ºC selama 1 jam dalam suatu tungku. Variasi komposisi yang dilakukan sebanyak tiga variasi. Selanjutnya kokas hasil karbonisasi dilakukan pengujian. Dari beberapa parameter kualitas kokas yang dipersyaratkan oleh PT ABC didapat hasil kadar air ketiga variasi sampel masih memiliki kadar air yang dipersyaratkan yaitu dibawah 4%, Kadar zat terbang ketiga variasi sampel masih memenuhi nilai yang dipersyaratkan yaitu

Simulation of Coke Quality Indicators Using Artificial Neural Network

The article shows the application of a neural network for modeling coke quality indicators Coke Reactivity Index (CRI) and Coke Strength after Reaction (CSR). Two optimization methods were used to train the neural network. The influence of the number of neurons on the simulation results was studied. The difference between experimental and calculated data on average does not exceed 2 %. The conclusion is made about the prospects of using a neural network to predict the values of CRI and CSR of coke. Keywords: artificial neural network, coke, coke reactivity index, coke strength after reaction