Biodiesel, i.e., a mixture of fatty acid methyl esters (FAMEs), produced from reacting triglyceride with methanol by alkali-catalyzed transesterification, has attracted much attention as an important renewable energy source. To aid in the optimization of biodiesel manufacturing, a number of published studies have applied commercial process simulators to quantify the effects of operating conditions on the process performance. Significantly, all of the reported simulation models are design models for new processes by fixing some level of equipment performance such as the conversion of transesterification reaction. Most models assume the feed oil as pure triolein and the biodiesel fuel as pure methyl oleate, and pay insufficient attention to the feed oil characterization, thermophysical property estimation, rigorous reaction kinetics, phase equilibrium for separation and purification units, and prediction of essential biodiesel fuel qualities. This paper presents first a comprehensive review of published literature pertaining to developing an integrated process modeling and product design of biodiesel manufacturing, and identifies those deficient areas for further development. This paper then presents new modeling tools and a methodology for the integrated process modeling and product design of an entire biodiesel manufacturing train (including transesterification reactor, methanol recovery and recycle, water wash, biodiesel recovery, glycerol separation, etc.). We demonstrate the methodology by simulating an integrated process to predict reactor and separator performance, stream conditions, and product qualities with different feedstocks. The results show that the methodology is effective not only for the rating and optimization of an existing biodiesel manufacturing, but also for the design of a new process to produce biodiesel with specified fuel properties.

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This paper presents a workflow to develop, validate, and apply a predictive model for rating and optimization of large-scale integrated refinery reaction and fractionation systems from plant data. We demonstrate the workflow with two commercial processes, a medium-pressure hydrocracking (MP HCR) unit with a feed capacity of 1 million tons per year and a high-pressure hydrocracking (HP HCR) unit with a feed capacity of 2 million tons per year in the Asia Pacific. The units include reactors, fractionators, and hydrogen recycle systems. With catalyst and hydrogen, the process converts heavy feedstocks, such as vacuum gas oil, into valuable low-boiling products, such as gasoline and diesel. We present the detailed procedure for data acquisition to ensure accurate mass balances and for implementing the workflow using Excel spreadsheets and a commercial software tool, Aspen HYSYS/Refining. Our procedure is equally applicable to other commercial software tools. The workflow includes special tools to facilitate an accurate transition from lumped kinetic components used in reactor modeling to the pseudo-components based on boiling point ranges required in the rigorous stage-by-stage simulation of fractionators. We validate the two models with 4 months of plant data, and the resulting models accurately predict unit performance, product yields, and fuel properties from the corresponding operating conditions. The MP HCR model predicts the yields of heavy naphtha, diesel fuel, and bottom products with average absolute deviations (AADs) of at most 3.4 wt %, predicts the specific gravities of heavy naphtha, diesel fuel, and bottom oil with AADs below 0.0184, predicts the flash point and freezing point of diesel fuel with AADs of 3.6 and 4.1°C, respectively, and predicts the outlet temperatures of catalyst beds with AADs of 1.9°C. The HP HCR model predicts the yields of liquefied petroleum gas (LPG), light naphtha, heavy naphtha, jet fuel, and residue oil with AADs below 1.7 wt %, predicts the specific gravities of light naphtha, heavy naphtha, jet fuel, and residue oil with AADs less than 0.134, predicts the flash point and freezing point of jet fuel with AADs of 1.6 and 2.3°C, respectively, and predicts the outlet temperatures of catalyst beds of the two HCR reactors with AADs of 1.8 and 3.2°C. We apply the validated plantwide model to quantify the effect of the H 2 /oil ratio on product distribution and catalyst life and the effect of HCR reactor temperature and feed flow rate on product distribution. The results agree well with experimental observations reported in the literature. Our resulting models only require typical operating conditions and routine analysis of feedstock and products and appear to be the only reported integrated HCR models that can quantitatively simulate all key aspects of reactor operation, fractionator performance, hydrogen consumption, product yield, and fuel properties.

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