Joshi Keshava, Navalgund Lokeshwari, Mubarak Nabisab Mujawar, Shet Vinayaka B
Department of Chemical Engineering, SDM College of Engineering and Technology, (Visvesvaraya Technological University Belagavi), Dharwad, Karnataka, 580 002, India.
Chemical and Energy Engineering, Faculty of Engineering, Universiti Teknologi Brunei, BE1410, Bandar Seri Begawan, Brunei Darussalam.
Sci Rep. 2025 Jul 14;15(1):25368. doi: 10.1038/s41598-025-11109-4.
Numerous technologies have been developed to remove nitrate from wastewater due to its significant health and environmental impacts. In the present study, an isolate of Pseudomonas syringae was utilized to investigate the denitrification rate using immobilized granular activated carbon (GAC) in a draft tube spouted bed reactor. Developing a theoretical model for this reactor requires an understanding of multiple interrelated parameters, many of which may vary under different operating conditions. Given the inherent nonlinearities of the system, such modelling can be both complex and resource-intensive. Artificial Neural Networks (ANNs) offer a promising solution for modelling such nonlinear systems due to their flexibility and generalization capabilities. In this work, a feed-forward backpropagation neural network with three layers was constructed, consisting of three input neurons, nine neurons in the hidden layer, and one output neuron. The ANN model effectively predicted the effluent nitrate concentration in the reactor, achieving a correlation coefficient of 98.8%, a root mean square error (RMSE) of 9.25 × 10%, an average absolute error of 0.57%, and a residual sum of squares (RSS) of 30.84. In comparison, the multiple regression analysis (MRA) model produced a lower correlation coefficient of 90%, a higher RMSE of 0.01%, an average absolute error of 1.32%, and an RSS of 166.63. These results demonstrate that the ANN model outperforms the regression model across all evaluated performance metrics, making it a more effective tool for capturing the complex dynamics of the denitrification process in the reactor.
由于硝酸盐对健康和环境有重大影响，人们已经开发了许多技术来去除废水中的硝酸盐。在本研究中，利用丁香假单胞菌的一个分离株，在导流管喷动床反应器中使用固定化颗粒活性炭（GAC）来研究反硝化速率。为该反应器建立理论模型需要了解多个相互关联的参数，其中许多参数在不同的操作条件下可能会有所不同。鉴于系统固有的非线性，这样的建模既复杂又耗费资源。人工神经网络（ANN）由于其灵活性和泛化能力，为这类非线性系统的建模提供了一个有前景的解决方案。在这项工作中，构建了一个具有三层的前馈反向传播神经网络，由三个输入神经元、隐藏层中的九个神经元和一个输出神经元组成。该人工神经网络模型有效地预测了反应器中流出物的硝酸盐浓度，相关系数达到98.8%，均方根误差（RMSE）为9.25×10%，平均绝对误差为0.57%，残差平方和（RSS）为30.84。相比之下，多元回归分析（MRA）模型的相关系数较低，为90%，RMSE较高，为0.01%，平均绝对误差为1.32%，RSS为166.63。这些结果表明，在所有评估的性能指标上，人工神经网络模型都优于回归模型，使其成为捕捉反应器中反硝化过程复杂动态的更有效工具。
