Impact of the forecast price on economic results for methanol production from olive waste

Fuel ◽  
2021 ◽  
Vol 295 ◽  
pp. 120631
Author(s):  
M. Puig-Gamero ◽  
J.R. Trapero ◽  
D.J. Pedregal ◽  
P. Sánchez ◽  
L. Sanchez-Silva
2021 ◽  
Vol 50 ◽  
pp. 101608
Author(s):  
Yaser Khojasteh-Salkuyeh ◽  
Omid Ashrafi ◽  
Ehsan Mostafavi ◽  
Philippe Navarri

2020 ◽  
Vol 45 (59) ◽  
pp. 34483-34493
Author(s):  
Hua Liu ◽  
Jinghui Qu ◽  
Ming Pan ◽  
Bingjian Zhang ◽  
Qinglin Chen ◽  
...  

Catalysts ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 1058 ◽  
Author(s):  
Leone Frusteri ◽  
Catia Cannilla ◽  
Serena Todaro ◽  
Francesco Frusteri ◽  
Giuseppe Bonura

Ternary CuxZnyAlz catalysts were prepared using the hydrotalcite (HT) method. The influence of the atomic x:y:z ratio on the physico-chemical and catalytic properties under CO2 hydrogenation conditions was probed. The characterization data of the investigated catalysts were obtained by XRF, XRD, BET, TPR, CO2-TPD, N2O chemisorption, SEM, and TEM techniques. In the “dried” catalyst, the typical structure of a hydrotalcite phase was observed. Although the calcination and subsequent reduction treatments determined a clear loss of the hydrotalcite structure, the pristine phase addressed the achievement of peculiar physico-chemical properties, also affecting the catalytic activity. Textural and surface effects induced by the zinc concentration conferred a very interesting catalyst performance, with a methanol space time yield (STY) higher than that of commercial systems operated under the same experimental conditions. The peculiar behavior of the hydrotalcite-like samples was related to a high dispersion of the active phase, with metallic copper sites homogeneously distributed among the oxide species, thereby ensuring a suitable activation of H2 and CO2 reactants for a superior methanol production.


2018 ◽  
Vol 42 ◽  
pp. 01004
Author(s):  
Andang W. Harto ◽  
Mella Soelanda

The rising of atmospheric CO2 concentration is the major source to global warming system. Many methods have been proposed to mitigate global warming, such as carbon penalty, carbon trading, CO2 sequestration, etc. However these proposed methods are usually uneconomical, i.e., these methods do not produce economic valuable substances. This paper will propose a method to absorb atmospheric CO2 to produce economic valuable substances such as methanol, dimethyl ether, ethylene, several hydrocarbon substances and derivatives and several graphite substances. This paper is focused on methanol production using atmospheric CO2 capture. The overall process is endothermic. Thus a sufficient energy source is needed. To avoid more CO2 emission, the energy source must not use conventional fuels. To assure the continuity of energy deliberation, nuclear energy will be used as the energy source of the process. In this paper, the Passive Compact Molten Salt Reactor (PCMSR) will be used as the energy source. The 460 MWth PCMSR is coupled with atmospheric CO2 capture, desalination, hydrogen production and methanol production facilities. The capturing CO2 capacity is 7.2 ton/h of atmospheric CO2. The valuable outputs of this system are 3.34 ton/h of H2, 34.56 ton/h of O2, 5.24 ton/h of methanol and 86.74 MWe of excess electricity.


2017 ◽  
Vol 42 (14) ◽  
pp. 9031-9043 ◽  
Author(s):  
N.S. Shamsul ◽  
S.K. Kamarudin ◽  
N.T. Kofli ◽  
N.A. Rahman

1997 ◽  
Vol 24 (4) ◽  
pp. 869-878 ◽  
Author(s):  
John R. Kirby ◽  
Christopher J. Kristich ◽  
Shelby L. Feinberg ◽  
George W. Ordal

2016 ◽  
Vol 52 (91) ◽  
pp. 13401-13404 ◽  
Author(s):  
B. Ipek ◽  
R. F. Lobo

Direct catalytic methanol production from methane is achieved on Cu-SSZ-13 zeolite catalysts using N2O as the oxidant.


Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3965
Author(s):  
Daniel Chuquin-Vasco ◽  
Francis Parra ◽  
Nelson Chuquin-Vasco ◽  
Juan Chuquin-Vasco ◽  
Vanesa Lo-Iacono-Ferreira

The objective of this research was to design a neural network (ANN) to predict the methanol flux at the outlet of a carbon dioxide dehydrogenation plant. For the development of the ANN, a database was generated, in the open-source simulation software “DWSIM”, from the validation of a process described in the literature. The sample consists of 133 data pairs with four inputs: reactor pressure and temperature, mass flow of carbon dioxide and hydrogen, and one output: flow of methanol. The ANN was designed using 12 neurons in the hidden layer and it was trained with the Levenberg–Marquardt algorithm. In the training, validation and testing phase, a global mean square (RMSE) value of 0.0085 and a global regression coefficient R of 0.9442 were obtained. The network was validated through an analysis of variance (ANOVA), where the p-value for all cases was greater than 0.05, which indicates that there are no significant differences between the observations and those predicted by the ANN. Therefore, the designed ANN can be used to predict the methanol flow at the exit of a dehydrogenation plant and later for the optimization of the system.


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