天然产物分离纯化过程中层析技术的研究——应用及相关过程模型化
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摘要
层析分离技术是应用最广泛的一种天然产物分离纯化技术。目前对该技术的研究可以概括为应用和理论研究两个方面,但两个方面的研究都存在着一些不足之处。在应用方面,现存层析工艺大多存在处理量低、应用成本较高等缺陷;在理论研究方面,则主要是缺乏完善的过程模拟计算方法。这些问题的存在严重阻碍了该技术的进一步发展。因此,建立高效、廉价、简便的层析工艺,完善相关层析过程的模型化研究具有非常重要的理论与实际意义。
本文探讨了大孔吸附树脂固定床层析技术在天然产物分离纯化中的应用,旨在建立一条低成本,适合工业化生产的工艺路线。并着重对相关层析过程进行了模型化研究,基于人工神经网络(ANN)强大的非线性系统描述能力,分别建立了ANN-大孔吸附树脂固定床传质动力学模型(ANN-Fixed bed)、ANN-高效液相色谱优化模型(ANN-HPLC)以及ANN-高速逆流色谱优化模型(ANN-HSCCC),进一步完善了层析过程的模拟计算方法。
本文首先建立了大孔吸附树脂固定床层析与结晶相结合的纯化工艺,并从热力学的角度考察了吸附过程的本质。通过工艺条件的优化,实现了茄尼醇的纯化,一次性将其纯度从50%左右提高到了94.51%,说明大孔吸附树脂固定床层析技术是实现工业化生产切实可行的办法。研究结果表明,高极性溶剂和低温环境有利于增大树脂的吸附量;增大料液浓度、降低流速、增大层析柱高径比,会提高柱吸附量。但浓度过高会降低溶质在柱床中的保留时间,流速过慢会使上样周期变长,柱子越长柱压会相应的增大;在洗脱过程中,随着洗脱剂极性的增大,产品纯度提高但收率降低,因此在本文中采用分步洗脱的方式;热力学研究表明,大孔树脂对茄尼醇的吸附为物理吸附,吸附过程能够自发进行,茄尼醇被树脂吸附后运动受到更大限制使系统变得更有序。以上研究结果可以为进一步将大孔吸附树脂固定床层析技术应用于天然产物的分离纯化提供有效的帮助。
为了更深入的认识大孔吸附树脂固定床层析过程,本文对其分离过程中的传质动力学进行了研究。改进了传统的普通速率模型(GR模型),用于描述层析过程中的穿透行为。相比传统的GR模型,本文从粒径分布(PSD)和等温线变化(VOI)两方面对模型进行了完善。模型计算结果表明,当GR模型只考虑PSD或只考虑VOI的时候,计算结果与实验结果有比较明显的差别。当模型没有考虑PSD的影响时,计算得到的穿透曲线斜率较大,穿透点提前,而且更早的达到平衡;当模型没有考虑到VOI时,计算得到的穿透曲线具有更小的斜率,穿透时间延长,达到平衡的时间延长;当将以上两方面的因素补充到GR模型后,计算结果与实验值更加接近。这些研究结果为进一步完善GR模型提供了参考。
鉴于GR模型表达式及求解过程相对复杂。本文建立了一种更加简单、准确的模型-ANN-Fixed bed模型,预测层析过程的穿透行为。结果表明,ANN-Fixed bed模型预测结果能够更好的与实验结果相吻合,线性相关系数R~2＞0.98,平均方差小于0.05。利用该模型对实验参数进行考察发现,随着原料液浓度的降低、吸附介质粒径的减小、吸附介质颗粒孔隙率的增加以及高径比的增加,穿透曲线变陡,达到穿透点所需要的时间变长;随着流速的增加以及柱床空隙率的增加,达到穿透点所需要的时间缩短。这些研究结果对于进一步优化层析工艺,提高层析吸附量具有很好的指导意义。
在建立ANN-HPLC模型过程中,本文首次通过序列的结合ANN和色谱响应函数(CRF)建立了一种新的化学计量学模型,用于色谱分离条件的优化。利用该模型,本文成功的一次性优化得到了适用于三种不同色谱分离目的的最优操作条件,并在预测的最优操作条件下得到了预期的色谱图,证明了该模型的有效性。该方法改善了以往模型灵活性不足的缺点,同时也为建立更加灵活、有效的色谱分离条件优化模型提供了新思路。
在建立ANN-HSCCC模型过程中,本文首次利用ANN探讨了逆流色谱分离过程中固定相的保留机理,通过对网络输入变量的考察发现,固定相保留率随着流动相流速和黏度的增加而减小,随着转速和溶剂体系上下相密度差的增加而增大,并且输入变量之间存在明显相互作用。在此基础上,通过结合Box-Behnken响应面模型与Derringer色谱响应函数,建立了逆流色谱分离条件优化模型。方差分析结果表明,p-value＜0.0001,R~2(Adj)＞0.96,说明建立的模型能够很好的描述本文所研究的体系。利用该模型,本文成功实现了对逆流色谱分离过程中分辨率和分析时间的综合优化。由于在逆流色谱分离过程中,一般需要较长的分析时间和消耗大量的溶剂,这些研究结果将有助于更加有效的优化逆流色谱分离过程,提高色谱分离效率。
Chromatography has become one of the most widely used techniques in separation and purification of natural substances. However, the relevant studies on this technique are not comprehensive, especially on the fundamental studies. The goal of this paper is to study the chromatographic processes widely used in natural substances separation and purification, including both application and fundamental aspects. In application research, an economical method combining adsorption chromatography and crystallization was developed. In modeling research, the artificial neural network (ANN) was used to simulate the chromatographic processes. In this paper, the ANN-flxed bed model, ANN-reverse phase liquid chromatography model (ANN-HPLC) and ANN-high speed counter current chromatography model (ANN-HSCCC) were developed.
In this paper, the separation of solanesol using fixed bed chromatography with macroporous resins was studied and then the solanesol was further purified by crystallization. By using this method, the purity of solanesol increased from 50% to 94.51%. The results showed that high-polarity solvent and low temperature were advantageous to the adsorption process; the stepwise elution was adopted in desorption process; the thermodynamic parameters such as enthalpy, Gibbs free energy and entropy changes were calculated and these values showed that solanesol adsorption process was exothermic and spontaneous. The results obtained in this part may provide scientific references for the large-scale solanesol production from tobacco leaves extracts.
The general rate model including particle size distribution (PSD) and variation of isotherm (VOI) was developed. To examine the validity of the model, the theoretical predictions were compared with the experimental data obtained at different conditions. The results showed that the theoretical predictions were well consistent with the experimental data. The calculated results also showed that apparent differences can be observed between experimental data and the simulated results when only the PSD or VOI was taken into account. The theoretical predictions by model without considering PSD showed that the breakthrough occurred earlier and approached the plateau concentration much faster. The theoretical predictions by model without considering VOI showed that the breakthrough curve became less steeper and the time required to reach breakthrough point and plateau concentration was delayed.
When developing the ANN- fixed bed model, the model was developed to describe the breakthrough behavior in fixed bed with macroporous resins. The encouraging simulated results showed that the ANN model could describe present system better than the modified general rate model. By using the predictive ability of ANN model, the influence of each experimental parameter was investigated. Predicted results showed that with the increases of particle porosity and the ratio of bed height to inner column diameter (ROHD), the breakthrough time was delayed. On the contrary, an increase in feed concentration, flow rate, mean particle diameter and bed porosity decreased the breakthrough time. When developing the ANN-HPLC model, an optimization strategy combining ANN and chromatographic response function (CRF) for chromatographic separation in HPLC was proposed. The ANN was used to simultaneously predict the resolution and analysis time, which are the two most important aspects in chromatographic separation. Subsequently, a CRF consisting of resolution and analysis time was used to predict the optimal operation conditions for different specialized purposes. The expected chromatograms were obtained at the predicted conditions, which verified the applicability of present method. Based on the results of this study, sequential combination of ANN and CRF can provide a more general, flexible and efficient optimization method for chromatographic separation.
When developing ANN-HSCCC model, the effects of separation parameters on retention of stationary phase (S_f), resolution and retention time were studied. The ANN was used to simultaneously predict the effects of operation conditions and physical properties of two-phase systems on S_f. It was found that more accurate predictions were achieved by means of the ANN. Subsequently, a chemometrics approach combining Box-Behnken response surface model and Derringer's desirability function was applied for simultaneous optimization of resolution and analysis time in HSCCC. The merging of the two parameters was accomplished using the Derringer's desirability function with subsequent optimization by a Box-Behnken response surface design. The developed model was checked by statistical analysis. By implementing the optimal conditions predicted by the validated model, enhanced resolution between two similar analytes was achieved in a reasonable time. The analyses and results obtained in this paper will benefit to improve the efficiency of CCC separation.
本文探讨了大孔吸附树脂固定床层析技术在天然产物分离纯化中的应用,旨在建立一条低成本,适合工业化生产的工艺路线。并着重对相关层析过程进行了模型化研究,基于人工神经网络(ANN)强大的非线性系统描述能力,分别建立了ANN-大孔吸附树脂固定床传质动力学模型(ANN-Fixed bed)、ANN-高效液相色谱优化模型(ANN-HPLC)以及ANN-高速逆流色谱优化模型(ANN-HSCCC),进一步完善了层析过程的模拟计算方法。
本文首先建立了大孔吸附树脂固定床层析与结晶相结合的纯化工艺,并从热力学的角度考察了吸附过程的本质。通过工艺条件的优化,实现了茄尼醇的纯化,一次性将其纯度从50%左右提高到了94.51%,说明大孔吸附树脂固定床层析技术是实现工业化生产切实可行的办法。研究结果表明,高极性溶剂和低温环境有利于增大树脂的吸附量;增大料液浓度、降低流速、增大层析柱高径比,会提高柱吸附量。但浓度过高会降低溶质在柱床中的保留时间,流速过慢会使上样周期变长,柱子越长柱压会相应的增大;在洗脱过程中,随着洗脱剂极性的增大,产品纯度提高但收率降低,因此在本文中采用分步洗脱的方式;热力学研究表明,大孔树脂对茄尼醇的吸附为物理吸附,吸附过程能够自发进行,茄尼醇被树脂吸附后运动受到更大限制使系统变得更有序。以上研究结果可以为进一步将大孔吸附树脂固定床层析技术应用于天然产物的分离纯化提供有效的帮助。
为了更深入的认识大孔吸附树脂固定床层析过程,本文对其分离过程中的传质动力学进行了研究。改进了传统的普通速率模型(GR模型),用于描述层析过程中的穿透行为。相比传统的GR模型,本文从粒径分布(PSD)和等温线变化(VOI)两方面对模型进行了完善。模型计算结果表明,当GR模型只考虑PSD或只考虑VOI的时候,计算结果与实验结果有比较明显的差别。当模型没有考虑PSD的影响时,计算得到的穿透曲线斜率较大,穿透点提前,而且更早的达到平衡;当模型没有考虑到VOI时,计算得到的穿透曲线具有更小的斜率,穿透时间延长,达到平衡的时间延长;当将以上两方面的因素补充到GR模型后,计算结果与实验值更加接近。这些研究结果为进一步完善GR模型提供了参考。
鉴于GR模型表达式及求解过程相对复杂。本文建立了一种更加简单、准确的模型-ANN-Fixed bed模型,预测层析过程的穿透行为。结果表明,ANN-Fixed bed模型预测结果能够更好的与实验结果相吻合,线性相关系数R~2＞0.98,平均方差小于0.05。利用该模型对实验参数进行考察发现,随着原料液浓度的降低、吸附介质粒径的减小、吸附介质颗粒孔隙率的增加以及高径比的增加,穿透曲线变陡,达到穿透点所需要的时间变长;随着流速的增加以及柱床空隙率的增加,达到穿透点所需要的时间缩短。这些研究结果对于进一步优化层析工艺,提高层析吸附量具有很好的指导意义。
在建立ANN-HPLC模型过程中,本文首次通过序列的结合ANN和色谱响应函数(CRF)建立了一种新的化学计量学模型,用于色谱分离条件的优化。利用该模型,本文成功的一次性优化得到了适用于三种不同色谱分离目的的最优操作条件,并在预测的最优操作条件下得到了预期的色谱图,证明了该模型的有效性。该方法改善了以往模型灵活性不足的缺点,同时也为建立更加灵活、有效的色谱分离条件优化模型提供了新思路。
在建立ANN-HSCCC模型过程中,本文首次利用ANN探讨了逆流色谱分离过程中固定相的保留机理,通过对网络输入变量的考察发现,固定相保留率随着流动相流速和黏度的增加而减小,随着转速和溶剂体系上下相密度差的增加而增大,并且输入变量之间存在明显相互作用。在此基础上,通过结合Box-Behnken响应面模型与Derringer色谱响应函数,建立了逆流色谱分离条件优化模型。方差分析结果表明,p-value＜0.0001,R~2(Adj)＞0.96,说明建立的模型能够很好的描述本文所研究的体系。利用该模型,本文成功实现了对逆流色谱分离过程中分辨率和分析时间的综合优化。由于在逆流色谱分离过程中,一般需要较长的分析时间和消耗大量的溶剂,这些研究结果将有助于更加有效的优化逆流色谱分离过程,提高色谱分离效率。
Chromatography has become one of the most widely used techniques in separation and purification of natural substances. However, the relevant studies on this technique are not comprehensive, especially on the fundamental studies. The goal of this paper is to study the chromatographic processes widely used in natural substances separation and purification, including both application and fundamental aspects. In application research, an economical method combining adsorption chromatography and crystallization was developed. In modeling research, the artificial neural network (ANN) was used to simulate the chromatographic processes. In this paper, the ANN-flxed bed model, ANN-reverse phase liquid chromatography model (ANN-HPLC) and ANN-high speed counter current chromatography model (ANN-HSCCC) were developed.
In this paper, the separation of solanesol using fixed bed chromatography with macroporous resins was studied and then the solanesol was further purified by crystallization. By using this method, the purity of solanesol increased from 50% to 94.51%. The results showed that high-polarity solvent and low temperature were advantageous to the adsorption process; the stepwise elution was adopted in desorption process; the thermodynamic parameters such as enthalpy, Gibbs free energy and entropy changes were calculated and these values showed that solanesol adsorption process was exothermic and spontaneous. The results obtained in this part may provide scientific references for the large-scale solanesol production from tobacco leaves extracts.
The general rate model including particle size distribution (PSD) and variation of isotherm (VOI) was developed. To examine the validity of the model, the theoretical predictions were compared with the experimental data obtained at different conditions. The results showed that the theoretical predictions were well consistent with the experimental data. The calculated results also showed that apparent differences can be observed between experimental data and the simulated results when only the PSD or VOI was taken into account. The theoretical predictions by model without considering PSD showed that the breakthrough occurred earlier and approached the plateau concentration much faster. The theoretical predictions by model without considering VOI showed that the breakthrough curve became less steeper and the time required to reach breakthrough point and plateau concentration was delayed.
When developing the ANN- fixed bed model, the model was developed to describe the breakthrough behavior in fixed bed with macroporous resins. The encouraging simulated results showed that the ANN model could describe present system better than the modified general rate model. By using the predictive ability of ANN model, the influence of each experimental parameter was investigated. Predicted results showed that with the increases of particle porosity and the ratio of bed height to inner column diameter (ROHD), the breakthrough time was delayed. On the contrary, an increase in feed concentration, flow rate, mean particle diameter and bed porosity decreased the breakthrough time. When developing the ANN-HPLC model, an optimization strategy combining ANN and chromatographic response function (CRF) for chromatographic separation in HPLC was proposed. The ANN was used to simultaneously predict the resolution and analysis time, which are the two most important aspects in chromatographic separation. Subsequently, a CRF consisting of resolution and analysis time was used to predict the optimal operation conditions for different specialized purposes. The expected chromatograms were obtained at the predicted conditions, which verified the applicability of present method. Based on the results of this study, sequential combination of ANN and CRF can provide a more general, flexible and efficient optimization method for chromatographic separation.
When developing ANN-HSCCC model, the effects of separation parameters on retention of stationary phase (S_f), resolution and retention time were studied. The ANN was used to simultaneously predict the effects of operation conditions and physical properties of two-phase systems on S_f. It was found that more accurate predictions were achieved by means of the ANN. Subsequently, a chemometrics approach combining Box-Behnken response surface model and Derringer's desirability function was applied for simultaneous optimization of resolution and analysis time in HSCCC. The merging of the two parameters was accomplished using the Derringer's desirability function with subsequent optimization by a Box-Behnken response surface design. The developed model was checked by statistical analysis. By implementing the optimal conditions predicted by the validated model, enhanced resolution between two similar analytes was achieved in a reasonable time. The analyses and results obtained in this paper will benefit to improve the efficiency of CCC separation.
引文
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