Techno-economic analysis of dimethylfuran (DMF) and hydroxymethylfurfural (HMF) production from pure fructose in catalytic processes

Kazi, Feroz Kabir; Patel, Akshay D.; Serrano-Ruiz, Juan Carlos; Dumesic, James A.; Anex, Robert P.

Techno-economic analysis of dimethylfuran (DMF) and hydroxymethylfurfural (HMF) production from pure fructose in catalytic processes

2011

Kazi, Feroz Kabir | Patel, Akshay D. | Serrano-Ruiz, Juan Carlos | Dumesic, James A. | Anex, Robert P.

Techno-economic studies of processes for production of dimethyl-furan (DMF) and hydroxymethyl-furfural (HMF) from fructose are performed based on published laboratory results. The early stage of technology development necessitates use of simplified process models to analyze a conceptual bio-refinery producing fuel and chemical intermediates. HMF and DMF production processes are divided into four and six processing areas respectively that include synthesis and purification sections. The HMF process employs a biphasic (aqueous–organic phases) continuously stirred tank reactor (CSTR), and the DMF process has an additional 3-train fixed bed catalytic plug flow tubular reactor (PFTR). The processing capacities are 300metric ton/day of fructose and the plants will operate for 20 years. Installed equipment costs are estimated (US$2007) as $102.4 million for the HMF and $121.9 million for the DMF process, respectively. The DMF process uses a copper–ruthenium–carbon (Cu–Ru/C) catalyst. Catalyst cost is estimated as $36.4 million for the first charge with replacement cost of $258,500/2-years. Cost analysis is performed following the discounted cash flow method. The minimum selling prices for HMF and DMF are estimated as $1.33/l ($5.03/gal) and $2.02/l ($7.63/gal), respectively. Sensitivity analyses are performed to assess the relative importance of key assumptions on minimum HMF and DMF selling prices. The most significant parameters are feedstock cost, product yields, by-product prices, catalyst cost and total purchased equipment costs. Process and economic uncertainties can be reduced by incorporating better performance (through laboratory and pilot trials) and cost data.

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