乙醛生产过程的数据处理和实时优化
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摘要
乙醛是一种重要的中间体，然而由于目前国内乙醛总产量不高，造成其售价偏高。利用先进的信息技术和自动化技术，优化现有乙醛装置的生产操作和管理，是提高国内乙醛装置生产能力的有效手段。本文对乙醛生产过程的实时在线操作优化进行了深入研究，解决了其中涉及的几个关键技术问题，包括稳态检测、数据调和、软测量建模等，并在工业现场实施投运了实时优化系统，取得了很好的经济效益。
本文的创新点和主要工作在于：
1．针对乙醛生产过程数据采集时间的多样性，提出了数据融合和稳态判断的联合算法；使用该算法对乙醛生产过程的数据进行融合、稳态检测，形成样本集合。
2．针对包含化学反应过程的生产流程，提出了基于原子衡算的数据调和算法，并将之应用于乙醛生产过程的数据调和。调和计算中考虑了粗乙醛流量和小放空气体流量的密度补偿。通过数据调和，分析并定位了流量测量中存在的大幅度零点误差，提高了测量系统的精度。
3．针对乙醛生产过程中的粗乙醛浓度，分别使用线性回归、非线性回归和主元分析方法建立软测量模型，开发了软测量系统。通过八个月的现场运行，不断地进行在线校正，软测量模型有效地测量出粗乙醛浓度值。提出了样本选取率、回归模型的一致性系数等概念。
4．根据上述软测量模型建立了乙醛收率模型，并开发了实时优化系统。实时优化操作系统使生产在保持平稳的前提下逐步调优，不断改进操作条件。通过四个月的实时优化实施，有效地提高了乙醛收率，促进了操作经验和优化理论的结合。并提出了基于经济目标的操作优化命题，建立了经济优化模型，讨论了该模型与其关键优化参数之间的关系。
Invention of the Wacker process, which is an industrial process for the commercially manufacture of acetaldehyde by directly oxidizing ethene, is a milestone to petrochemical industry. It provides the basis for industrial-scale production of acetic acid, acetone, ethyl acetate, ketene, acetic anhydride, butyraldehyde and vinyl acetate. Optimizing the process operation and management by means of advanced information technology and automation technology is an efficient approach to increase acetaldehyde's production capability. The real-time optimization(RTO) scheme was studied in this thesis, with focus on the crucial problems of steady state detection, data reconciliation, soft modeling and optimization implementation. A practical operation optimization system was developed and applied to the real plant.
The main contributions of the work can be summarized in the following:
1. It is pointed out that data preprocessing is of great importance to modeling and optimization. A unified algorithm for data merge and steady state detection was presented.
2. For reaction process, a data reconciliation arithmetic based on atoms was developed. To get mass flow rate of vent gas and coarse aeetaldehyde from their volume flow rate data, density compensations are performed with regressed relationship between density and (temperature, pressure and concentration). Gross error exiting in flow rate was identified during the calculation. Quality of measurements was improved and balanced data were provided as an important basis for modeling and optimization.
3. The soft-sensing model of the concentration of coarse aeetaldehyde was established. Two important concepts of model verification were proposed, ratio of selection for modeling and coefficient of coincidence. The model is updated on hourly basis to compensate the mismatch continually. The whole system was implemented and was kept running for eight months. The average deviation between the predicted values and the off-line laboratory analysis results is about 1.36 percent. The model is effective and practical, and is ready for optimization use.
4. Formulation and implementation of yield optimization were intensively studied. The effect of proposed RTO scheme was validated in an overburdened real chemical plant, which implies the effectiveness and practicability of the technique. RTO would make profit for aeetaldehyde production process 6,862,000 yuan RMB per year. Furthermore, the optimization problem was re-formulated with economics introduced. Some key parameters of the model and their impact on overall economics were discussed.
本文的创新点和主要工作在于：
1．针对乙醛生产过程数据采集时间的多样性，提出了数据融合和稳态判断的联合算法；使用该算法对乙醛生产过程的数据进行融合、稳态检测，形成样本集合。
2．针对包含化学反应过程的生产流程，提出了基于原子衡算的数据调和算法，并将之应用于乙醛生产过程的数据调和。调和计算中考虑了粗乙醛流量和小放空气体流量的密度补偿。通过数据调和，分析并定位了流量测量中存在的大幅度零点误差，提高了测量系统的精度。
3．针对乙醛生产过程中的粗乙醛浓度，分别使用线性回归、非线性回归和主元分析方法建立软测量模型，开发了软测量系统。通过八个月的现场运行，不断地进行在线校正，软测量模型有效地测量出粗乙醛浓度值。提出了样本选取率、回归模型的一致性系数等概念。
4．根据上述软测量模型建立了乙醛收率模型，并开发了实时优化系统。实时优化操作系统使生产在保持平稳的前提下逐步调优，不断改进操作条件。通过四个月的实时优化实施，有效地提高了乙醛收率，促进了操作经验和优化理论的结合。并提出了基于经济目标的操作优化命题，建立了经济优化模型，讨论了该模型与其关键优化参数之间的关系。
Invention of the Wacker process, which is an industrial process for the commercially manufacture of acetaldehyde by directly oxidizing ethene, is a milestone to petrochemical industry. It provides the basis for industrial-scale production of acetic acid, acetone, ethyl acetate, ketene, acetic anhydride, butyraldehyde and vinyl acetate. Optimizing the process operation and management by means of advanced information technology and automation technology is an efficient approach to increase acetaldehyde's production capability. The real-time optimization(RTO) scheme was studied in this thesis, with focus on the crucial problems of steady state detection, data reconciliation, soft modeling and optimization implementation. A practical operation optimization system was developed and applied to the real plant.
The main contributions of the work can be summarized in the following:
1. It is pointed out that data preprocessing is of great importance to modeling and optimization. A unified algorithm for data merge and steady state detection was presented.
2. For reaction process, a data reconciliation arithmetic based on atoms was developed. To get mass flow rate of vent gas and coarse aeetaldehyde from their volume flow rate data, density compensations are performed with regressed relationship between density and (temperature, pressure and concentration). Gross error exiting in flow rate was identified during the calculation. Quality of measurements was improved and balanced data were provided as an important basis for modeling and optimization.
3. The soft-sensing model of the concentration of coarse aeetaldehyde was established. Two important concepts of model verification were proposed, ratio of selection for modeling and coefficient of coincidence. The model is updated on hourly basis to compensate the mismatch continually. The whole system was implemented and was kept running for eight months. The average deviation between the predicted values and the off-line laboratory analysis results is about 1.36 percent. The model is effective and practical, and is ready for optimization use.
4. Formulation and implementation of yield optimization were intensively studied. The effect of proposed RTO scheme was validated in an overburdened real chemical plant, which implies the effectiveness and practicability of the technique. RTO would make profit for aeetaldehyde production process 6,862,000 yuan RMB per year. Furthermore, the optimization problem was re-formulated with economics introduced. Some key parameters of the model and their impact on overall economics were discussed.
引文
Anonymous(1996), Wacker: Shaping the future. Chemical Week Custom Manufacturing Profiles Supplement, May 15, pp. S24
Bonavita, N., Martini, R. and Matsko, T.(1999), Multivariable predictive control of an extractive distillation column. Automazione e Strumentazione, 47(4): 135-44
Brosillow(1978), Inferential control of process. AICHE J, 24(3): 485-509
Bussani, G., Chiari, M., etc. (1995), Application of Data-Reconcilation and Optimization Procedure to Hydrogen Plant. Computers chem. Engng., Vol. 19 Supp1., S299
Crowe, C. M. (1997), Data Reconcilation——Progress and Challenges. Process Control, 6(2): 89-98
Fillon, M., M. Meyer, H. Pingaud, and X. Joulia (1995), Data Reconciliation Based on Elemental Balances Applied to Batch Experiments. Computer chem. Engng., Vol. 19, Suppl, pp. S293-S298
Gertler, J. and Monajemy, R. (1995), Generating directional residuals with dynamic parity relations. Automatica, 31(4), 627-635
Gonzalez G. D.(1999), Soft sensors for processing plants. Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials. Piscataway, NJ, USA, 59-69
Gonzalez G. D (1994). Issues in sonft-sensor applications industrial plants. Proceedings of 1994 IEEE International Symposium on Industrial Electronics. New York, USA, 380-385
Guilandoust Mt, etc. (1987), Adaptive Inferential Control. IEEE Trans. on Neural Networks, 134(3): 171-179
Hwang D. S., Chang S. K. and Hsu P. L. (1997), A practical design for a robust fault detection and isolation system. International Journal of System Science, 28(3), 265-275
Islam, K. A., G. H. Weiss, J. A. Romagnoli(1994), Non-Linear Data Reconciliation for an Industrial Pyrolysis Reactor, Computers chem. Engng., Vol. 18, suppl., S217
Kuehen, D. R. and Davidson, H. (1961), Computer Control Ⅱ: Mathematics of Control. Chemical Engineering Progress. 57(6): 44
Lundstrom, F., Skogestad, S. (1995), Opportunities and difficulties with 5*5 distillation control. Journal of Process Control Conference, 5(4): 249-261
M.F, M.M, H. P and X.JOULIA (1995), Data Reconciliation Based on Elemental Balances Applied to Batch Experiments. Computer chem..Engng. Vol. 19, Suppl, pp. S293-S298
Madron, F., V. Veverka, and V. Vanecek(1977), Statistical Analysis of Material Balance of a Chemical Reactor, AICHE J., 23, 482
Michele De Laurentiis and Peeter M. Ravdin. (1994), Survival analysis of censored data: Neural network analysis detection of complex interactions between varivable. Breast
Cancer Resarch and Treament, 32,113-118
Murthy, A. K. S. (1973), A Least-Squares Solution to Mass Balance around a Chemical Reactor, Ind. Eng. Chem. Process. Des. Dev., 12,246
Pages, A., H. Pingaud, M. Meyer, and X. Joulia (1994), A Strategy for Simultaneous Data Reconciliation and Parameter Estimation on Process Flowsheets, Computers Chem. Engng., 18, Suppl., S223
Pierucci, S., T. Faravelli, and P. Brandani(1994), A Project for On-line Reconciliation and Optimization of an Olefin Plant, Computers chem. Engng., 18, Supp1., S241
Tham M. T., Montague G. A. (1991), Soft-sensors for process estimation and inferential control. J Process Control, 1(1): 3-14
Wang Yong-sheng, Shao Hui-he(2002). Soft sensors in fluid catalytic crocking. Mao Jian-qin and Cao Xi-ren. Proceedings of the 3rd World Congress on Intelligent Control and Automation. Hefei: The Press of the Univenity of Science & Technology of China, 1492-1496
Wilkinson D. J., Morris A. J., and Tham M. T. (1994), Multivariable constrained predictive control(with application to high performance distillation). International Journal of Control, 59(3): 841-862
竺建敏(1995)，高级过程控制和闭环实时优化，石油炼制与化工，26(3)
罗传义、王树清、欧阳福承、杨维新、金光日(1997)，乙醛生产装置提高生产能力的可行性探讨，14(1)：6～10
解淑成(1994)，乙醛装置催化剂活性的控制，扬子石油化工，9(3)：11～14
梅原忠(1975)，日本化学志，1975(6)：1097～1101
梅原忠(1976)，日本化学志，1976(12)：2248～2250
张学岑(1990)，乙醛生产过程的优化和自适应控制(硕士学位论文)，浙江大学
李浦(1989)，乙醛生产过程自适应优化控制(硕士学位论文)，浙江大学v
蒋慰孙，俞金寿(1986)，化工过程动态数学模型，化学工业出版社
曾文华(2001)，原油减压蒸馏塔侧线温度多变量预测控制，信息与控制，30(1)：89～92，96
徐国忠、冯新国等(2002)，先进控制技术在催化裂化分馏塔中的应用，石油炼制与化工，33(2)：39～42
何京(2003)，PI在纸浆造纸企业的应用，天津造纸，25(1)：24～25
李代芳、郭武(1999)，实时数据库PI及其在长炼的应用，化工自动化及仪表，26(3)：30～32
范伟云(1998)，反应网络的物料平衡一致性校验，化学工程，26(1)：36-38
李红军、秦永胜(1997a)，化工过程中的数据协调及显著误差检测，化工自动化及仪表，24(2)：25～32
李红军、秦永胜(1997b)，数据校正在精馏塔系中的应用，化工自动化及仪表，24(1)：30～36
陈剑、古勇、苏宏业、褚健(2000)，化工过程数据校正中的数据分类问题，仪器仪表学报，21(6)
王秀萍、荣冈、王树青(1999)，先进控制技术及应用-过程数据校正技术，化工自动化及仪表，26(3)：62～67
袁咏根(1986)，化工过程的数据校正和参数估计，化学工程，1986(3)
张仲广、邵之江(2001)，一种基于非线性规划的数据校正联合算法，厦门大学学报(自然科学版)，S1期2001．S1
俞金寿(2002)，工业过程先进控制，北京：中国石化出版社，270
于静江、周春晖(1996)，过程控制中的软测量技术，控制理论与应用，21(4)：139～143
桑林刚(1999)，生产过程数据处理与软测量建模方法研究，上海：华东理工大学硕士论文
王正中(1997)，复杂系统仿真及其应用，计算机世界
张杰、阳宪惠(2000)，多变量统计过程控制，化学工业出版社
钱积新、邵之江、董浩、张余岳、顾永如(1997)，工业自动化发展的必由之路—商品化软件的开发研究，炼汕化工自动化，159(5)：1～4
程国梁(2000)，FTA法在乙醛装置安全生产中的应用，石油化工安全技术
吴元春(2001)，乙醛生产过程优化，南京化工大学学报，23(4)
扬子石化公司乙醛／醋酸装置培训讲义(1985)
罗忠禹(1989)，一步法乙醛反应装置的模拟及压力对生产能力的影响，吉林化工学院学报，6(1)
梁军(1999)，大型板坯加热炉中钢坯温度分布的软测量研究及实现，仪器仪表学报，20(1)：81～83
皮道映(1997)，制浆造纸生产中的软测量技术——一个值得重视的课题，中国造纸学报，1997(12)：117～120
李向阳、朱学峰、刘焕彬(2001)，制浆蒸煮过程中基于预测误差估计器的软测量方法研究与应用，控制理论与应用，18(5)：722～725
骆晨钟、邵惠鹤(1999)，软仪表技术及其工业应用，仪表技术与传感器，1999(1)：32～34
牛林、杨承志(1999)，软测量技术应用于乙醛精馏塔成分估计，昆明理工大学学报，24(6)：60～62
仲蔚(2000)，分馏塔产品质量估计MIM0软测量，自动化仪表，2000(3)：19～22
马立平(2000)，多元线性回归分析——现代统计分析方法的学与用(十)，北京统计，2000(10)：38～39
Bonavita, N., Martini, R. and Matsko, T.(1999), Multivariable predictive control of an extractive distillation column. Automazione e Strumentazione, 47(4): 135-44
Brosillow(1978), Inferential control of process. AICHE J, 24(3): 485-509
Bussani, G., Chiari, M., etc. (1995), Application of Data-Reconcilation and Optimization Procedure to Hydrogen Plant. Computers chem. Engng., Vol. 19 Supp1., S299
Crowe, C. M. (1997), Data Reconcilation——Progress and Challenges. Process Control, 6(2): 89-98
Fillon, M., M. Meyer, H. Pingaud, and X. Joulia (1995), Data Reconciliation Based on Elemental Balances Applied to Batch Experiments. Computer chem. Engng., Vol. 19, Suppl, pp. S293-S298
Gertler, J. and Monajemy, R. (1995), Generating directional residuals with dynamic parity relations. Automatica, 31(4), 627-635
Gonzalez G. D.(1999), Soft sensors for processing plants. Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials. Piscataway, NJ, USA, 59-69
Gonzalez G. D (1994). Issues in sonft-sensor applications industrial plants. Proceedings of 1994 IEEE International Symposium on Industrial Electronics. New York, USA, 380-385
Guilandoust Mt, etc. (1987), Adaptive Inferential Control. IEEE Trans. on Neural Networks, 134(3): 171-179
Hwang D. S., Chang S. K. and Hsu P. L. (1997), A practical design for a robust fault detection and isolation system. International Journal of System Science, 28(3), 265-275
Islam, K. A., G. H. Weiss, J. A. Romagnoli(1994), Non-Linear Data Reconciliation for an Industrial Pyrolysis Reactor, Computers chem. Engng., Vol. 18, suppl., S217
Kuehen, D. R. and Davidson, H. (1961), Computer Control Ⅱ: Mathematics of Control. Chemical Engineering Progress. 57(6): 44
Lundstrom, F., Skogestad, S. (1995), Opportunities and difficulties with 5*5 distillation control. Journal of Process Control Conference, 5(4): 249-261
M.F, M.M, H. P and X.JOULIA (1995), Data Reconciliation Based on Elemental Balances Applied to Batch Experiments. Computer chem..Engng. Vol. 19, Suppl, pp. S293-S298
Madron, F., V. Veverka, and V. Vanecek(1977), Statistical Analysis of Material Balance of a Chemical Reactor, AICHE J., 23, 482
Michele De Laurentiis and Peeter M. Ravdin. (1994), Survival analysis of censored data: Neural network analysis detection of complex interactions between varivable. Breast
Cancer Resarch and Treament, 32,113-118
Murthy, A. K. S. (1973), A Least-Squares Solution to Mass Balance around a Chemical Reactor, Ind. Eng. Chem. Process. Des. Dev., 12,246
Pages, A., H. Pingaud, M. Meyer, and X. Joulia (1994), A Strategy for Simultaneous Data Reconciliation and Parameter Estimation on Process Flowsheets, Computers Chem. Engng., 18, Suppl., S223
Pierucci, S., T. Faravelli, and P. Brandani(1994), A Project for On-line Reconciliation and Optimization of an Olefin Plant, Computers chem. Engng., 18, Supp1., S241
Tham M. T., Montague G. A. (1991), Soft-sensors for process estimation and inferential control. J Process Control, 1(1): 3-14
Wang Yong-sheng, Shao Hui-he(2002). Soft sensors in fluid catalytic crocking. Mao Jian-qin and Cao Xi-ren. Proceedings of the 3rd World Congress on Intelligent Control and Automation. Hefei: The Press of the Univenity of Science & Technology of China, 1492-1496
Wilkinson D. J., Morris A. J., and Tham M. T. (1994), Multivariable constrained predictive control(with application to high performance distillation). International Journal of Control, 59(3): 841-862
竺建敏(1995)，高级过程控制和闭环实时优化，石油炼制与化工，26(3)
罗传义、王树清、欧阳福承、杨维新、金光日(1997)，乙醛生产装置提高生产能力的可行性探讨，14(1)：6～10
解淑成(1994)，乙醛装置催化剂活性的控制，扬子石油化工，9(3)：11～14
梅原忠(1975)，日本化学志，1975(6)：1097～1101
梅原忠(1976)，日本化学志，1976(12)：2248～2250
张学岑(1990)，乙醛生产过程的优化和自适应控制(硕士学位论文)，浙江大学
李浦(1989)，乙醛生产过程自适应优化控制(硕士学位论文)，浙江大学v
蒋慰孙，俞金寿(1986)，化工过程动态数学模型，化学工业出版社
曾文华(2001)，原油减压蒸馏塔侧线温度多变量预测控制，信息与控制，30(1)：89～92，96
徐国忠、冯新国等(2002)，先进控制技术在催化裂化分馏塔中的应用，石油炼制与化工，33(2)：39～42
何京(2003)，PI在纸浆造纸企业的应用，天津造纸，25(1)：24～25
李代芳、郭武(1999)，实时数据库PI及其在长炼的应用，化工自动化及仪表，26(3)：30～32
范伟云(1998)，反应网络的物料平衡一致性校验，化学工程，26(1)：36-38
李红军、秦永胜(1997a)，化工过程中的数据协调及显著误差检测，化工自动化及仪表，24(2)：25～32
李红军、秦永胜(1997b)，数据校正在精馏塔系中的应用，化工自动化及仪表，24(1)：30～36
陈剑、古勇、苏宏业、褚健(2000)，化工过程数据校正中的数据分类问题，仪器仪表学报，21(6)
王秀萍、荣冈、王树青(1999)，先进控制技术及应用-过程数据校正技术，化工自动化及仪表，26(3)：62～67
袁咏根(1986)，化工过程的数据校正和参数估计，化学工程，1986(3)
张仲广、邵之江(2001)，一种基于非线性规划的数据校正联合算法，厦门大学学报(自然科学版)，S1期2001．S1
俞金寿(2002)，工业过程先进控制，北京：中国石化出版社，270
于静江、周春晖(1996)，过程控制中的软测量技术，控制理论与应用，21(4)：139～143
桑林刚(1999)，生产过程数据处理与软测量建模方法研究，上海：华东理工大学硕士论文
王正中(1997)，复杂系统仿真及其应用，计算机世界
张杰、阳宪惠(2000)，多变量统计过程控制，化学工业出版社
钱积新、邵之江、董浩、张余岳、顾永如(1997)，工业自动化发展的必由之路—商品化软件的开发研究，炼汕化工自动化，159(5)：1～4
程国梁(2000)，FTA法在乙醛装置安全生产中的应用，石油化工安全技术
吴元春(2001)，乙醛生产过程优化，南京化工大学学报，23(4)
扬子石化公司乙醛／醋酸装置培训讲义(1985)
罗忠禹(1989)，一步法乙醛反应装置的模拟及压力对生产能力的影响，吉林化工学院学报，6(1)
梁军(1999)，大型板坯加热炉中钢坯温度分布的软测量研究及实现，仪器仪表学报，20(1)：81～83
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