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Dynamic Modelling of Batch Distillation Columns Chemical Dynamic Modelling of Batch Distillation Columns Maria Nunes de Almeida Viseu Thesis to obtain the Master of Science Degree in Chemical Engineering Supervisors: Prof. Dr Carla Isabel Costa Pinheiro Dr Charles Brand Examination Committee Chairperson: Prof. Dr Sebastião Manuel Tavares da Silva Alves Supervisor: Prof. Dr Carla Isabel Costa Pinheiro Members of the Committee: Prof. Dr João Miguel Alves da Silva November 2014 Page intentionally left blank ii Para os meus pais, Com amor. iii Page intentionally left blank iv Abstract atch distillation is becoming increasingly important in specialty product industries in which flexibility is a B key performance factor. Because high added value chemical compounds are produced in these industries with uncertain demands and lifetimes, mathematical models that predict separation times and product purities thereby facilitating plant scheduling are required. The primary purpose of this study is thus to develop a batch multi-staged distillation model based on mass and energy balances, equilibrium stages and tray hydraulic relations. The mathematical model was implemented in gPROMS ModelBuilder®, an industry- leading custom modelling and flowsheet environment software. Preliminary steps were undertaken prior to implementing the dynamic multi-staged model: batch distillation operating policies as well as modelling and tray hydraulic considerations were covered in a broad background review; a theoretical separation example of an equimolar benzene/toluene mixture was used to validate a simpler Rayleigh distillation model comprising only one equilibrium stage; tray hydraulic correlations encompassing column diameter, tray holdup and tray pressure drop estimations were tested in a methanol/water continuous separation case study. The multi-staged batch model was validated for a methanol/water separation using literature data from an experimental pilot plant and from theoretical results given by a model implemented in Fortran language and by commercial simulator Batchsim of Pro/II. A sensitivity analysis was performed to evaluate the model robustness, testing the effect of the reflux ratio and the heat duty on the separation time and methanol recovery. The results simulated in ModelBuilder for the batch multi-staged model reveal a 6.2% overestimation of the experimental methanol recovery. A very good agreement is found between the ModelBuilder and Fortran models: the methanol recovery predicted by ModelBuilder is only 2.3% lower. It is shown that the ModelBuilder multi-staged batch model is robust with ±10% heat duty variations or ±0.5 reflux ratio differences both affecting the total experiment time in approximately 12%. Differences of ±0.5 in the reflux ratio are found to have a 2.2 to 6.4% absolute impact on the methanol recovery whilst this recovery is practically not affected by 10% heat duty variations. This work offers a tool that may be applied to the scheduling of batch chemical plants and aid industrial management at the planning level. Keywords: Batch distillation; equilibrium stages; computational models; gPROMS v Page intentionally left blank vi Resumo importância da destilação descontínua tem vindo a aumentar em indústrias de química fina onde a A flexibilidade é um factor-chave de performance. Na medida em que produzem compostos químicos de alto valor acrescentado, são necessários modelos matemáticos que permitam determinar o tempo de separação e a pureza dos produtos, facilitando o planeamento industrial. O objectivo principal deste estudo consiste no desenvolvimento de um modelo dinâmico para colunas de destilação com vários andares baseado em balanços mássicos e energéticos e andares de equilíbrio. O modelo matemático foi implementado utilizando o software gPROMS ModelBuilder®. Antes de o desenvolver, realizaram-se as seguintes etapas: ampla revisão bibliográfica sobre modos de operação, modelos e relações hidráulicas de perdas de pressão em colunas batch; validação de um modelo simplificado Rayleigh de um só andar de equilíbrio, com base num exemplo teórico de separação de uma mistura equimolar benzeno/tolueno; teste de correlações de estimativa de diâmetro, holdup e perdas de pressão nos pratos, aplicadas a um estudo de caso de destilação em contínuo para a mistura metanol/água. Validou-se o modelo para uma separação metanol/água, utilizando dados experimentais publicados na literatura provenientes de uma unidade piloto, e também resultados teóricos gerados por um modelo desenvolvido em linguagem Fortran e pelo simulador comercial Batchsim Pro/II. Realizou-se também uma análise de sensibilidade para avaliar a robustez do modelo, testando o efeito da razão de refluxo e do calor fornecido ao ebulidor no tempo total de separação e na recuperação de metanol. Os resultados simulados no software ModelBuilder para o modelo sobrestimam em 6.2% a recuperação de metanol face ao seu valor experimental. Há uma notável concordância entre os dados gerados pelo ModelBuilder e pelo Fortran: a recuperação de metanol prevista pelo ModelBuilder é apenas 2.3% inferior. Demonstra-se que o modelo ModelBuilder é robusto com variações de ±10% no calor fornecido ao ebulidor ou diferenças de ±0.5 na razão de refluxo a afectar o tempo de separação em cerca de 12%. Diferenças de ±0.5 na razão de refluxo têm um impacto absoluto entre 2.2 a 6.4% na recuperação de metanol, enquanto aquela não é praticamente afectada por variações de 10% no calor fornecido. Espera-se com este estudo desenvolver uma ferramenta com aplicação na planificação de indústrias químicas com processos de destilação em descontínuo contribuindo, assim, para uma melhor gestão e controlo fabris. Palavras-chave: Destilação em descontínuo; andares de equilíbrio; modelos computacionais; gPROMS vii Page intentionally left blank viii Acknowledgements I would like to express my gratitude to my supervisors Prof. Carla Pinheiro and Dr Charles Brand for their continuous technical guidance, constructive criticism and friendly support during the production of this thesis. I would also like to acknowledge Prof. Dr Costas Pantelides and Dr Maarten Nauta for the opportunity to work at Process System Enterprise and for their valuable knowledge and availability. A special thanks to Inês and Prisci, with whom I shared my stay in London. I am sincerely thankful for their valuable friendship and for the time we spent together. A warm thanks goes to Mariana, Renato and Artur, for their encouragement and support. I am grateful for all the adventures and fun we experienced. Thank you João for sharing your life with me during our lovely university years. I am truly grateful to my parents, brother and sister, for their unconditional love. ix Page intentionally left blank x Contents 1 Introduction .................................................................................................................................................................. 1 1.1. Motivation ........................................................................................................................................................... 1 1.2. State of the art .................................................................................................................................................... 2 1.3. Original contributions ....................................................................................................................................... 3 1.4. Thesis outline ...................................................................................................................................................... 3 2 Literature review........................................................................................................................................................... 5 2.1. Batch distillation operating policies ................................................................................................................ 5 2.2. Distillation modelling ........................................................................................................................................ 9 2.2.1. Equilibrium stage model .............................................................................................................................. 9 2.2.2. Stage efficiency ............................................................................................................................................ 10 2.2.3. Two-film model .......................................................................................................................................... 11 2.2.4. Rate-based stage model ............................................................................................................................. 12 2.2.5. Maxwell-Stefan formulation ..................................................................................................................... 14 2.3. Tray design and operation ............................................................................................................................. 15 2.3.1. Tray design .................................................................................................................................................. 15 2.3.2. Tray operation ............................................................................................................................................. 18 3 Materials and methods ............................................................................................................................................
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