Cinar Ozer, Hasar Halil, Kinaci Cumali
Kahramanmaraş Sütçü Imam University, Department of Environmental Engineering, Campus of Avsar, 46001 Kahramanmaraş, Turkey.
J Biotechnol. 2006 May 17;123(2):204-9. doi: 10.1016/j.jbiotec.2005.11.002. Epub 2005 Dec 6.
A submerged membrane bioreactor receiving cheese whey was modeled by artificial neural network and its performance over a period of 100 days at different solids retention times was evaluated with this robust tool. A cascade-forward network was used to model the membrane bioreactor and normalization was used as a preprocessing method. The network was fed with two subsets of operational data, with two-thirds being used for training and one-third for testing the performance of the artificial neural network. The training procedure for effluent chemical oxygen demand (COD), ammonia, nitrate and total phosphate concentrations was very successful and a perfect match was obtained between the measured and the calculated concentrations. The results of the confirmation (or testing) procedure for effluent ammonia and nitrate concentrations were very successful; however, the results of the confirmation procedure for effluent COD and total phosphate concentrations were only satisfactory.
采用人工神经网络对处理奶酪乳清的浸没式膜生物反应器进行建模，并使用这个强大的工具评估了其在不同固体停留时间下100天内的性能。使用级联前向网络对膜生物反应器进行建模，并将归一化用作预处理方法。该网络输入了两个运行数据子集，其中三分之二用于训练，三分之一用于测试人工神经网络的性能。出水化学需氧量（COD）、氨、硝酸盐和总磷浓度的训练过程非常成功，实测浓度与计算浓度完美匹配。出水氨和硝酸盐浓度的验证（或测试）过程结果非常成功；然而，出水COD和总磷浓度的验证过程结果仅令人满意。
