A coal-based coproduction process of liquefied natural gas (LNG) and methanol (CTLNG-M) is developed and key units are simulated in this paper. The goal is to find improvements of the low-earning coal to synthesis natural gas (CTSNG) process using the same raw material but producing a low-margin, single synthesis natural gas (SNG) product. In the CTLNG-M process, there are two innovative aspects. Firstly, the process can co-generate high value-added products of LNG and methanol, in which CH₄ is separated from the syngas to obtain liquefied natural gas (LNG) through a cryogenic separation unit, while the remaining lean-methane syngas is then used for methanol synthesis. Secondly, CO₂ separated from the acid gas removal unit is partially reused for methanol synthesis reaction, which consequently increases the carbon element utilization efficiency and reduces the CO₂ emission. In this paper, the process is designed with the output products of 642,000 tons/a LNG and 1,367,800 tons/a methanol. The simulation results show that the CTLNG-M process can obtain a carbon utilization efficiency of 39.6%, bringing about a reduction of CO₂ emission by 130,000 tons/a compared to the CTSNG process. However, the energy consumption of the new process is increased by 9.3% after detailed analysis of energy consumption. The results indicate that although electricity consumption is higher than that of the conventional CTSNG process, the new CTLNG-M process is still economically feasible. In terms of the economic benefits, the investment is remarkably decreased by 17.8% and an increase in internal rate of return (IRR) by 6% is also achieved, contrasting to the standalone CTSNG process. It is; therefore, considered as a feasible scheme for the efficient utilization of coal by Lurgi gasification technology and production planning for existing CTSNG plants.

Biorefinery processes are challenged to make effective use of commercial flowsheeting software. Challenges include the lack of property data, complexity of raw materials, and emerging non-conventional processes and technologies. Surrogate models could assist by combining data-based models (by means of surrogate models) with conventional models available from flowsheeting vendors. The challenge would be to translate experimental data into property and process parameters compatible with models used by commercial software. The paper introduces an iterative approach for the systematic evaluation of such parameters. Degrees of freedom include options for pseudo-components, thermodynamic methods, and process models. The approach is applied for the modeling of a real-life biorefinery. Three case studies demonstrate the potential of the proposed scheme. The first case study has fixed options for the property and process models, while the second one has them as a degree of freedom. The third case study extrapolates the resulted metamodels to different capacities and six different feedstock types (wheat and rice straw, wood, sugarcane bagasse, banana stem, and miscanthus). Degrees of freedom expand as the cases address limited options for surrogate models, gradually incorporating additional options and, apparently, better regression results. The proposed framework could embed additional levels of sophistication using machine learning and artificial intelligence technology. The emphasis of the paper stands on the methodological framework and its demonstration with real-life examples.