da Costa Albuquerque Clarissa Daisy, de Campos-Takaki Galba Maria, Fileti Ana Maria Frattini
Departamento de Estatística e Informática, Universidade Católica de Pernambuco, Rua Nunes Machado, 42, Bloco J, Térreo, Boa Vista, 50050-590, Recife, Pernambuco, Brazil.
J Ind Microbiol Biotechnol. 2008 Nov;35(11):1425-33. doi: 10.1007/s10295-008-0443-5. Epub 2008 Sep 26.
Biomass is an important variable in biosurfactant production process. However, such bioprocess variable, usually, is collected by sampling and determined by off-line analysis, with significant time delay. Therefore, simple and reliable on-line biomass estimation procedures are highly desirable. An artificial neural network model (ANN) is presented for the on-line estimation of biomass concentration, in biosurfactant production by Candida lipolytica UCP 988, as a nonlinear function of pH and dissolved oxygen. Several configurations were evaluated while developing the optimal ANN model. The optimal ANN model consists of one hidden layer with four neurons. The performance of the ANN was checked using experimental data. The results obtained indicate a very good predictive capacity for the ANN-based software sensor with values of R2 of 0.969 and RMSE of 0.021 for biomass concentration. Estimated biomass using the ANN was proved to be a simple, robust and accurate method.
生物量是生物表面活性剂生产过程中的一个重要变量。然而，这种生物过程变量通常是通过采样收集并通过离线分析来确定的，存在显著的时间延迟。因此，非常需要简单可靠的在线生物量估计程序。本文提出了一种人工神经网络模型（ANN），用于在线估计解脂耶氏酵母UCP 988生产生物表面活性剂过程中的生物量浓度，该模型将生物量浓度作为pH值和溶解氧的非线性函数。在开发最优ANN模型时评估了几种配置。最优ANN模型由一个具有四个神经元的隐藏层组成。使用实验数据检查了ANN的性能。获得的结果表明，基于ANN的软件传感器具有非常好的预测能力，生物量浓度的R2值为0.969，RMSE值为0.021。事实证明，使用ANN估计生物量是一种简单、稳健且准确的方法。
