Screening for solvents in Extractive Distillation considering Quantitative Risk Analysis

Transcription

1 Screening for solvents in Extractive Distillation considering Quantitative Risk Analysis Nancy Medina-Herrera, Arturo Jiménez-Gutiérrez and Ignacio Grossmann. Department of Chemical Engineering, Instituto Tecnológico de Celaya Department of Chemical Engineering, Carnegie Mellon University Abstract In general, solvent selection for extractive distillation is done taking into account the solvent performance in terms of equipment cost and its capability to alter the relative volatility of the original mixture. This work presents an approach for solvent selection that, in addition to economic terms, includes safety measures determined through a Chemical Process Quantitative Risk Analysis. Potential solvents are first found through the use of the ProCAMD tool within the ICAS Software, and then a multi-objective optimization procedure based on a derivative free optimization (DFO) procedure is implemented. Societal risk (SR) and total annual cost (TAC) are used as the two objective functions. The optimization procedure was carried out within MATLAB, with ASPEN Plus simulations working as a black box within the DFO, and a genetic algorithm recursively used to find the set of optimal tradeoffs (or Pareto front) between both objectives. As a result, the best potential solvents for a given separation are identified. A case study is used to show the application of the proposed approach. Keywords: Distillation, Process safety, Quantitative Risk Analysis, Solvent Selection Introduction Extractive distillation systems are used to separate two principal types of mixtures: (1) nearboiling point and (2) azeotropic mixtures. Such complex distillation systems reach highly purified products by adding an additional component, or entrainer, commonly a liquid solvent. In addition to the design variables for conventional distillation systems, the solvent selection is a major design decision, which is not straightforward because of potential trade-offs between design criteria. Even when most solvents are potentially dangerous, especially for flammable incidents, solvent selection is commonly made based only on economic performance. 1 A more integral decision-making process should be implemented. In other words, the design of equipment should not only consider economic factors but also additional aspects such as safety. An initiative to consider safety into solvent selection was proposed by Patel et al. 2 Solvents show different performance in distillation columns. The objective of this work is to develop an integrated approach for solvent screening for extractive distillation systems including risk and economics as optimization objectives. First, a pre-selection step using molecular design software is implemented. Then, a multi-objective optimization procedure is carried out. The strategy is based on a derivative-

2 free optimization (DFO) procedure. 3 In order to find optimal tradeoff between cost and safety, we use a quantitative risk analysis (QRA) following the CCPS guidelines 4. Proposed approach Figure 1 shows a representation of the proposed approach. In the pre-selection step, we use ICAS software 5 to find potential solvents that meet specified safety properties. The substitution principle 6 is applied here to find inherently safer solvents. For the optimization step, we developed a toolkit within MATLAB for the multi-objective optimization, based on a DFO procedure, and attenuation and moderation principles are applied. ASPEN Plus works as a black box, taking as input the new values of the design variables assigned by the genetic algorithm and giving as output the risk value from the QRA model. Then, the QRA model is evaluated to quantify societal risk, and the total annual cost is evaluated. Pareto fronts are obtained, showing the optimal compromise between both criteria. Safety properties User properties Molecular Design ProCAMD ICAS Pre-Selection Initial Guess Potential Solvents New values for design variables Optimization Design Model ASPEN Plus ASPEN Plus output become QRA input QRA Model Objective functions Sharing ASPEN Plus and QRA output MATLAB Pareto Fronts Inherently Safer Design Figure 1 Approach ICAS is based on a computer aided molecular design procedure, and can take into account several properties. In this work we focus on three safety properties: flash point, octanol-water partition coefficient and lethal concentration. The flash point is related to flammability hazard, while octanol/water partition coefficient and lethal concentration are related to toxicity hazard. Table 1 shows the values accepted for OSHA 7 to consider a material as non hazardous. In addition, we specified that the solvent should not form new azeotropes and that its boiling point be higher than that of the heavy component of the mixture to be separated.

3 Table 1 Safety properties values Property Flash Temperature 7 Value >323 K Log P 7 <3.5 LC 50 7 >2 mg Boiling point > No formation of a new azeotrope The multi-objective optimization is based on the ε-constraint method. The problem is formulated by minimizing the total annual cost ( ) subject to different levels of acceptable risk: St.: The QRA model includes a frequency and consequences analyses. The equations for the MESH model are solved with the use of the ASPEN Plus process simulator. A frequency analysis is done using bow tie graphs and the potential catastrophic events and their probability of occurrence are identified and evaluated. The most common failures considered in the analysis are ruptures in medium and large pipes, and in process vessel. A consequences analysis models the source of released material, the dispersion in the environment, the characterization of the events, and their effects. Five catastrophic scenarios are considered in the QRA for all the system units. The total risk is the sum of each scenario risk. Risk is a combination of probability and consequences; societal risk is related to workers life loss as consequences. Case Study An extractive distillation system is considered for the separation of bioethanol. The feed has a flowrate of 100 Kmol/hr of an ethanol/water mixture with a molar composition of 0.6/0.4. The product purity specification is dehydrated ethanol at 99%vol. We applied our approach to find potential inherently safer solvents taking into account safety and economic criteria. The specified values for safety properties are show in Table 1, with =100 C. The results of the pre-selection step using ProCAMD are presented in Figure 2. ICAS found 18 potential entrainers that meet the required safety properties. Figure 2 shows the flash temperature and partition coefficient values of the 18 potential components found by ProCAMD. Some of the components are not

4 Flash Temperature (open cup) K produced on an industrial scale, so we restricted our consideration to those components available in the ASPEN Database. The three selected components are dimethylformamide, ethylene glycol, and 2- (2-methoxyethoxy) ethanol. ProCAMD-ICAS Results Figure 1 Pre-Selection Results 430 Octanol/water coefficient (Log P) Figure 3 shows the Pareto front obtained from the multi-objective optimization for the three solvents. Dimethylformamide is the safest solvent but also the most expensive. On the other hand, ethylene glycol is the cheapest but the most dangerous solvent. From the shape of the Pareto curves, one can noticed that ethylene glycol provides the less sensitive designs when considering both objectives. The societal risk for the three solvents considered ranges from 35/1000 to 47/1000 dead workers.

5 Total Annual Cost (TAC, $/yr) x Societal Risk (SR, victims/yr) Dimethylformamide Ethylene Glycol 2-(2-methoxyethoxy)ethanol Figure 2 Pareto Curves Conclusions We have presented a new approach for solvent selection and design of optimal extractive distillation systems. Pro-CAMD is used as a tool for finding entrainer candidates. A multi-objective optimization method allows the development of Pareto graphs that show the best potential solvents, taking into account risk in addition to the economic measures commonly used for the design of these types of designs. Also, the use of a DFO approach for the optimization task represents an alternative to deterministic techniques that may show convergence problems. Acknowledgement The authors thank Professor Rafiqul Gani for allowing the use of the ICAS software for the development of this work.

6 References 1. Luyben WL, Chien IL. Design and Control of Distillation Systems for Separating Azeotropes: Wiley; Patel SJ, Ng D, Mannan MS. Inherently safer design of solvent processes at the conceptual stage: Practical application for substitution. Journal of Loss Prevention in the Process Industries. 2010;23(4): Conn AR, Scheinberg K, Vicente LN. Introduction to Derivative-free Optimization: Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104); Safety CfCP. Guidelines for Chemical Process Quantitative Risk Analysis (2nd Edition): Center for Chemical Process Safety/AIChE; Harper PM, Gani R. A multi-step and multi-level approach for computer aided molecular design. Computers & Chemical Engineering. 2000;24(2-7): Kletz TA. Process Plants: A Handbook for Inherently Safer Design: Taylor & Francis; OSHA. Hazard Communication -71: (Proposed rule making).. In: Register IOotF, ed. Washington, D.C.2006.