Early Stage Design of a Biorefinery from Castor Oil

Mario R. Eden, John D. Siirola and Gavin P. Towler (Editors) Proceedings of the 8 th International Conference on Foundations of Computer-Aided Process Design – FOCAPD 2014 July 13-17, 2014, Cle Elum, Washington, USA © 2014 Elsevier B.V. All rights reserved.

Early Stage Design of a Biorefinery from Castor Oil Daniela de Faria,a Alberto Quaglia,b Fernando L. P. Pessoa,a,* Rafiqul Ganib a

Escola de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941909, Brazil b CAPEC, Department of Chemical and Biochemical Engineering, Denmark Technical University, DK-2800 Kgs Lyngby, Denmark [email protected]

Abstract This paper presents a systematic method for synthesis and analysis of biomass based biorefinery pathways (process networks) in terms of current and future market conditions. The systematic method has been implemented into a computer aided tool that is able to quickly evaluate alternatives and network scenarios. The tool integrates data collection, modelling and superstructure optimization to determine the optimal network for a biorefinery. The application of the synthesis-analysis method and its corresponding computer aided tool is highlighted for a case study where castor oil is the specified biomass available for the biorefinery. Keywords: castor oil, superstructure optimization, integrated business and engineering framework, separation process synthesis.

1. Introduction In recent years, numerous research efforts have been made in the development of biorefineries for the sustainable production of fuels and chemicals from renewable feedstocks. The many and increasing number of processing alternatives proposed for the conversion of various types of biomass means that there is a constant need for screening and selection of the best alternative for each application scenario. It is therefore necessary to consider all viable alternatives when looking for the optimal solution.. Brazil is in a unique position with respect to setting-up of biorefineries because of its large potential supply of different types of biomass. Castor oil, which is a non-edible vegetable oil, has the potential to play a major role in the expansion of biorefineries. It is easy to cultivate; has low production costs; has valuable chemicals content together with high-value derivatives. Castor seed is a natural candidate to become one of the major crops in Brazil. It is rich in ricinoleic acid and, due to the chemical versatility of this acid, has many derivatives with extensive industrial applications. Its current major uses are for: manufacturing of paints, inks, varnishes, coatings, cosmetics, and lubricants (Ogunniyi, 2006). Until now, the majority of oleochemical industries in Brazil have either focused on the generation of chemicals (e.g., for the cosmetic industry) or biofuels (e.g., biodiesel). The production chain is not integrated, even though both final products come from the same starting material. The use of castor oil in biorefineries needs to be investigated. This paper deals with the issues encountered in the early stage design of biorefineries with multiple products (chemicals as well as fuels) from castor oil as the biomass. This is the first work encountered that applies superstructure optimization into a castor oil

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biorefinery synthesis. The biorefineries must be economically feasible subject to current market conditions. In this work, the integrated business and engineering framework developed by Quaglia et al. (2012), which offers a flexible framework for quick evaluation of alternatives within a superstructure optimization scheme, has been adopted for the solution of the biorefinery synthesis problem. Once an optimal network has been found it is further refined in terms of a more detailed flowsheet through the process synthesis method of Jaksland et al. (1995). The framework has been adapted to the specific problem and applied to develop the biorefinery network from castor oil. First a brief overview of the methodology is given, followed by the presentation of the application of the methodology to the biorefinery case study, which is divided into two parts: problem definition and analysis of results. The case study involves the use of castor oil as the biomass under various scenarios of market conditions.

2. Methodology The integrated business and engineering framework of Quaglia et al. (2012) integrates decisions at strategic levels, such as business and engineering, for the synthesis and design of processing networks. It is adapted in this work for biorefinery network optimization. The solution from superstructure optimization identifies the optimal raw material, product portfolio and process technologies and configurations for a specified market scenario, together with the optimal material flows throughout the identified network. The adapted algorithm has 5-steps. 2.1. Step-by-step Algorithm Step-1: problem definition. Here, the synthesis problem is defined in terms of scope, scenario descriptions, objective function and performance criteria, which to be used for evaluation of alternatives. Step-2: data collection and superstructure definition. The available and relevant industrial, commercial and regulatory information is collected and gathered in a database together with knowledge of different processing alternatives to convert the raw materials into the potential products identified in step-1. All this information is processed through a tool to generate a superstructure (see Fig.(1), where the numbers 133 at the top stand for the process steps and the numbers in the boxes indicate the process intervals available for each process step). Step-3: model selection & validation. Each processing interval within a processing step (see Fig.(1)) is represented by a generic mathematical model through which a mass balance of the network can be performed. As developed by Quaglia et al. (2012), the model allows networks within networks, recycle of streams and many more options. The application of the selected models for each interval requires model parameters that are determined from the collected data in step-2. More details of these models can be found in Quaglia et al. (2012). Step-4: superstructure optimization. Based on the problem definition, the data and the models selected, a MILP or MINLP problem is formed in GAMS (GAMS Development Corporation, 2011) and solved to obtain the biorefinery network model. Step-5: Flowsheet generation and validation. The optimized network obtained from step-4 is based on simple mass balance calculations. Using this result as a basis, the process synthesis method of Jaksland et al. (1995) is applied to generate a corresponding flowsheet that can be validated and further optimized through detailed process simulation. In principle, any process simulator can be used in this step.

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Figure 1 – Biorefinery superstructure with optimal flowsheet selected (the filled boxes in grey).

2.2. Superstructure The superstructure consists of a number of processing steps (vertical columns) and within each processing steps, there can be any number of process alternatives, depending on the available technologies considered. The intervals are linked to each other through connections represented by binary variables. Once the models for each process intervals have been selected and the logical constraints added, the total number of alternatives represented by the superstructure can easily be calculated.

3. Biorefinery Case Study: Problem Definition 3.1. Problem Definition The objective is to find the optimal biorefinery network with castor oil (CastorOil.in, 2010, Comprehensive Castor Oil Report) as the selected biomass. Fig.(2) shows the definition of the castor oil, the principal conversion steps and the chemical products that can be obtained. 3.2. Data Collection and Superstructure Definition For each of the conversions shown in Fig.(1), the corresponding conversion data has been collected. This data includes the reaction condition, the conversion, the catalyst used, the utilities needed, etc. The detailed data collected can be obtained from the corresponding author. The superstructure shown in Fig.(1) is obtained from the collected data and the process steps shown in Fig.(2). For example, Process step 1 corresponds to the raw material, which in our case is mainly one component. Due to the high content of ricinoleic acid in castor oil (approximately 90%), the oil is being considered here as 90% of the triglyceride of this fatty acid (triricinolein) and 10% of other triglycerides. Process step 2 is responsible for a reaction to split triricinolein from the other triglyderides, which are then separated as a waste stream; and dividing the flow of castor oil (triricinolein) between the process steps. The process steps are classified as follows: 3 to 10 stands for Hydrolysis; 12 to 17 for Transesterification/Esterification; 18 to 24 for Hydrogenation; 25 to 27 for Alkali Fusion; and 28 to 32 for Pyrolysis. Step 11 is the flow divider for ricinoleic acid, and each process interval in step 33 stands for a potential final product for the biorefinery

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presented: glycerol, biodiesel, low quality 12-HSA (mixture of 80 % 12-HSA, 3 % ricinoleic acid, 14 % stearic acid and 3 % oleic acid), 12-HSA, undecylenic acid, HCO, heptaldehyde, 2-octanol, sebacic acid and ricinoleic acid. 3.3. Model Selection & Validation The search space for this design problem involves a great number of equations and data needed for its simulation. In order to deal with the complexity generated by the size of the problem and the amount of data required to solve it, some simplifying assumptions have been made. In this analysis, it is considered that all unreacted raw material related to a key reactant and all unreacted chemical utility are recycled after the reaction. Due to the lack of data available in the literature for castor oil derivatives, it is assumed that only sharp separations could occur in the process, during the superstructure optimization phase. Assuming all separations to be sharp allowed the formulation of the “multi-stream” problem as a MILP problem, instead of a MINLP problem. Several logical constraints have been included in the model to exclude impossible or infeasible solutions. An example is equation (1) that represents a logical constraint for selecting only those process intervals that have a possible connection with a previous process interval already selected:

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where ykk is a binary variable associated with the selection of process intervals (1 if it is selected and 0 if it is not), Sk,kk is a parameter describing all possible connections between interval k and kk in the superstructure (1 if the connection exists and 0 if it does not). This prevents the solution from selecting an interval that has no inlet flow.

4. Biorefinery Case Study: Optimal Solution 4.1. Superstructure Optimization The design problem, formulated as a MILP optimization, consists of 954,602 equations; 938,094 continuous variables; and 86 discrete variables. The problem is solved in GAMS using the solver CPLEX in 6.2 seconds to maximize the biorefinery profit. The superstructure optimization solution selected the process intervals highlighted (filled interval boxes) in Fig.(1). The optimal products portfolio consists of glycerol, 12HSA, HCO, undecylenic acid, heptaldehyde, 2-octanol, sebacic acid and ricinoleic acid. Biodiesel was not chosen due to its low market price and low demand (biodiesel from castor oil has low demand). Transesterification/ Esterification Unit

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Pyrolysis Unit Alkali Fusion Unit Hydrolysis Unit

Figure 2 – Biorefinery flow diagram.

Biodiesel 12-Hydroxystearic Acid Hydrogenated Castor Oil Undecylenic Acid Heptaldehyde Glycerol 2-Octanol Sebacic Acid Ricinoleic Acid

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All the processing alternatives selected pointed that it is better to use straight castor oil as a feedstock rather than the isolated ricinoleic acid. The second best solution (not shown in Fig.(1) and for which the objective function only differs by 0.78 % from the first one), though, chose ricinoleic acid as the starting material for alkali fusion unit; hence for further analysis it would be wise to investigate both results. The technologies for the conversion of raw materials into products were chosen as follows: enzymatic hydrolysis for ricinoleic acid (and glycerol) in 3-3 (Goswami et al., 2013); castor oil hydrogenation with H2 for HCO in 18-4 (Maskaev et al., 1971); HCO hydrolysis for 12-HSA (and glycerol) in 23-1 (Maskaev et al., 1971); castor oil pyrolysis for undecylenic acid (and heptaldehyde) in 25-1 (Das et al., 1989); and castor oil alkali fusion for sebacic acid (and 2-octanol) in 28-1 (Vasishtha et al., 1990). 4.2. Flowsheet Generation and Validation The calculated values for the optimal flow split ratios and biorefinery interval inlet and outlet streams are given in Table 1. The limit in flow for products has been estimated as 50% of global demand and 12-HSA, HCO, heptaldehyde, 2-octanol and ricinoleic acid reached this upper limit. Despite being the most valuable product for the optimal biorefinery, undecylenic acid outlet is not very high, since its by-product (heptaldehyde) has a low market demand. The same happens for sebacic acid and 2-octanol. The synthesis of process separation techniques and the detailed modelling for the selected flowsheet was conducted for each unit. The separation techniques are screened according to their pure component property analysis and the best order of split (we have 3 separation tasks for 4 components in this case) obtained is shown in Fig.(3), along with all the separation methods considered and the detailed screening process. The best technique to separate all 4 components is found to be distillation. Hence process simulation was performed by integrating the optimal material flows obtained in the superstructure optimization with the data of distillation columns for the separation tasks. Table 1: List of variables and their optimal values. Variable Castor oil inlet (1-1) Glycerol outlet (33-1) 12-HSA outlet (33-4) HCO outlet (33-6) Undecylenic acid outlet (33-5) Heptaldehyde outlet (33-7) 2-Octanol outlet (33-8) Sebacic acid outlet (33-9) Ricinoleic acid outlet (33-10) Hydrolysis unit (3-3) Hydrogenation unit (18-4) Castor oil flow split (2-1) Pyrolysis unit (25-1) Alkali fusion unit (28-1) 12-HSA production (20-4) HCO flow split (19-4) Market (33-6)

Optimal value 2834 kg/h 169 kg/h 694 kg/h 694 kg/h 163 kg/h 69 kg/h 69 kg/h 107 kg/h 694 kg/h 0.282 0.560 0.093 0.065 0.566 0.434

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T1, T2, T3, T6, T8, T9, T10, T11, T12, T14, T15, T16, T17, T18, T19, T20, T22, T23 T3, T6, T9, T11, T16, T17, T18, T19, T23 T3, T6, T9, T16, T17, T23 T3, T6, T9

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23 All techniques considered 18 No solid separation needed 12 Data for pure component properties available 9 O Only feasible techniques 8 Techniques feasible for adjacent pairs 6 Lack of data for membranes 4 Separation of contaminants not needed 3 No azeotropes 2 No gases 1 Countercurrent ccontactt

T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15, T16, T17, T18, T19, T20, T21, T22, T23 T3, T6, T8, T9, T10, T11, T15, T16, T17, T18, T19,T23 T3, T6, T9, T11, T16, T17, T19, T23 T3, T6, T9, T17,

ACRO ACRO HEP UA

T6, T9 ACRO HEP UA RRR

Separation task 1 RRR

Separation task 2 HEP UA

HEP Separation task 3 UA

Figure 3 – Screening of separation techniques & best order of separation tasks for Pyrolysis Unit. Note: T1, T2,…, T23 stands for: absorption, azeotropic distillation, cryogenic distillation, crystrallization, desublimation, distillation, drying, extractive distillation, flash operation, foam fractionation, gas separation membranes, ion exchange, leaching, liquid-liquid extraction, liquid membranes, microfiltration, molecular sieve adsorption, partial condensation, pervaporation, stripping, sublimation, supercritical extraction, ultrafiltration. ACRO, HEP, UA, RRR stands for: acrolein, heptaldehyde, undecylenic acid and triricinolein.

5. Conclusion An optimization based framework for process network synthesis and design has been adopted and successfully applied for the synthesis of a castor oil biorefinery. Through the framework, the problem of biorefinery design has been formulated and solved, leading to the identification, for the present market scenario, of the optimal biorefinery configuration along with its optimal product portfolio: glycerol, 12-HSA, HCO, undecylenic acid, heptaldehyde, 2-octanol, sebacic acid and ricinoleic acid.

References G. Das, R. Trivedi, A. Vasishtha, 1989, Heptaldehyde and Undecylenic Acid From Castor Oil, JAOCS, 66, 938-941. GAMS Development Corporation, 2013, GAMS GDX facilities and tools. GAMS Development Corporation. Available at . Accessed 12.10.13. D. Goswami, R. Sen, J. Basu, S. De, 2010, Surfactant Enhanced Ricinoleic Acid Production Using Candida Rugosa Lipase, Bioresource Technology, 101, 6-13. C. Jaksland, R. Gani, K. Lien, 1995, Separation Process Design and Synthesis Based on Thermodynamic Insights, Chemical Engineering Science, 50, 511-530. A. Maskaev, N. Man’Kovskaya, L. Lend’el, V. Fedorovskii, E. Simurova, V. Terent’eva, 1971, Preparation of 12-Hydroxystearic Acid: The Raw Material for Plastic Greases, Khimiya i Tekhnologiya Topliv i Masel, No. 2, 21-24. D. Ogunniyi, 2006, Castor oil: A Vital Industrial Raw Material, Bioresource Technology, 97, 1086-1091. A. Quaglia, B. Sarup, G. Sin, R. Gani, 2012, Integrated Business and Engineering Framework for Synthesis and Design of Enterprise-Wide Processing Networks, Computers and Chemical Engineering, 38, 213-223. A. Vasishtha, R. Trivedi, G. Das, 1990, Sebacic Acid and 2-Octanol from Castor Oil, JAOCS, 67, 333-337.