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.

To optimize biodiesel manufacturing, many reported studies have built simulation models to quantify the relationship between operating conditions and process performance. For mass and energy balance simulations, it is essential to know the four fundamental thermophysical properties of the feed oil: liquid density (ρ L ), vapor pressure (P vap ), liquid heat capacity (C p L ), and heat of vaporization (ΔH vap ). Additionally, to characterize the fuel qualities, it is critical to develop quantitative correlations to predict three biodiesel properties, namely, viscosity, cetane number, and flash point. Also, to ensure the operability of biodiesel in cold weather, one needs to quantitatively predict three low-temperature flow properties: cloud point (CP), pour point (PP), and cold filter plugging point (CFPP). This article presents the results from a comprehensive evaluation of the methods for predicting these four essential feed oil properties and six key biodiesel fuel properties. We compare the predictions to reported experimental data and recommend the appropriate prediction methods for each property based on accuracy, consistency, and generality. Of particular significance are (1) our presentation of simple and accurate methods for predicting the six key fuel properties based on the number of carbon atoms and the number of double bonds or the composition of total unsaturated fatty acid methyl esters (FAMEs) and (2) our posting of the Excel spreadsheets for implementing all of the evaluated accurate prediction methods on our group website (www.design.che.vt.edu) for the reader to download without charge.ii Acknowledgement

This work presents the methodology to develop, validate, and apply a predictive model for an integrated fluid catalytic cracking (FCC) process. We demonstrate the methodology using data from a commercial FCC plant in the Asia Pacific with a feed capacity of 800 000 tons per year. Our model accounts for the complex cracking kinetics in the riserÀregenerator with a 21-lump kinetic model. We implement the methodology with Microsoft Excel spreadsheets and a commercial software tool, Aspen HYSYS/ Petroleum Refining from Aspen Technology, Inc. The methodology is equally applicable to other commercial software tools. This model gives accurate predictions of key product yields and properties given feed qualities and operating conditions. In addition, this work presents the first lumped FCC kinetic model integrated with a gas plant model in the literature. We validate this work using 6 months of plant data. We also perform several case studies to show how refiners may apply this work to improve the gasoline yield and increase unit throughput. A key application of the integrated FCC model is to generate DELTAÀBASE vectors for linear programming (LP)-based refinery planning to help refiners choose an optimum slate of crude feeds. DELTAÀBASE vectors quantify changes in FCC product yields and properties as functions of changes in feed and operating conditions. Traditionally, refiners generated DELTAÀBASE vectors using a combination of historical data and correlations. Our integrated model can eliminate guesswork by providing more robust predictions of product yields and qualities. This work differentiates itself from previous work in this area through the following contributions: (1) detailed models of the entire FCC plant, including the overhead gas compressor, main fractionator, primary and sponge oil absorber, primary stripper, and debutanizer columns, (2) process to infer molecular composition required for the kinetic model using routinely collected bulk properties of feedstock, (3) predictions of key liquid product properties not published alongside previous related work (density, ASTM D86 distillation curve, and flash point), (4) case studies showing industrially useful applications of the model, and (5) application of the model with an existing LP-based planning tool.