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Sustainable tropical fruit peel waste biochars for enhanced cadmium and lead adsorption: mechanistic insights and optimization using response surface methodology and backpropagation neural networks
(2025-08-01)
Limmun, Wanida
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Limmun, Warunee
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Maneesri, Wisit
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Pewpa, Orrawan
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Chungcharoen, Thatchapol
Heavy metal contamination, particularly from cadmium (Cd(II)) and lead (Pb(II)), presents a severe environmental challenge due to its toxicity and persistence. This study explores an innovative approach by utilizing abundant yet underutilized tropical fruit peel waste to produce biochars that serve as effective, sustainable adsorbents for heavy metal remediation. Biochars derived from banana peels (BP) and Monthong durian shells (DS) were synthesized via pyrolysis at 400–800 °C and evaluated for their physicochemical properties and adsorption efficiency. The DS600 biochar exhibited the highest adsorption capacity, removing Cd(II) (40.37 mg/g) and Pb(II) (51.74 mg/g), surpassing BP600 (40.22 mg/g and 47.23 mg/g, respectively). This study introduces a dual-modeling framework by integrating response surface methodology (RSM) with backpropagation neural network (BPNN) to optimize adsorption conditions and enhance predictive accuracy. The optimized conditions achieved over 99% removal efficiency, with R<sup>2</sup> > 0.98 and MSE < 0.05, confirming the robustness of the model-based predictions. The study highlights the superior adsorption performance of DS600 biochar, with adsorption mechanisms influenced by pH, dosage, and biochar properties. In contrast to conventional studies that focus solely on equilibrium adsorption or rely on statistical models, this work pioneers the use of tropical fruit peel biochar in heavy metal remediation, providing quantitative insights into process optimization and practical scalability. The findings demonstrate the potential for valorizing agricultural waste into high-performance adsorbents, advancing cost-effective and sustainable water treatment technologies.
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Backpropagation Neural Network with Adaptive Learning Rate for Classification
(2023-01-01)
Jullapak, Rujira
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Thammano, Arit
This research aims to improve the classification accuracy by modifying an original backpropagation neural network. In the proposed BPNN-ZMP, the learning rates were automatic tuned to improve the classification accuracy. Breast Cancer Coimbra dataset and Banknote Authentication dataset were used for testing the model performances. The results demonstrate that BPNN-ZMP improved over the original backpropagation neural network by 12.12 and 11.46% for Breast Cancer Coimbra dataset and Banknote Authentication dataset respectively. Although BPNN-ZMP could improve the model accuracy, the high accuracy in neural network backpropagation has been challenged in future work.
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A Hybrid Neural Network for Predictive Model in A Plastic Injection Molding Process
(2022-04-01)
Kiatcharoenpol, Tossapol
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Klongboonjit, Sakon
A reliable and sensitive technique for predicting quality of a plastic work-piece produced in injection molding process is essential help for practicing engineers. A system based on the process parameters that can estimate both two prime characteristics, %volume shrinkage and warpage of work-piece before it produced is significantly beneficial. In this paper, a fast feed forward network, Hybrid Neural Network (HNN), is proposed to construct the predictive model for those two quality characteristics. The unique algorithm of HNN based on the optimization of the weights of each layer is changed to a linear problem by linearization of the sigmoid functions. As iteration procedure used in Backpropagation algorithm is eliminated, the network training time is significant reduced. With this fast convergence of using HNN, the intelligent predictive model for injection molding process that can learn online is possible for further study. To entitle the network to cater for various process parameter conditions, a knowledge base as training and testing data have to be generated on the experimental data in a comprehensive working range of a plastic injection molding process. Consequently, the experiments were performed in 256 conditions based on the combination of nine basic process parameters. The neural networks were trained and the architecture of networks was appropriately selected by benchmarking the Root Mean Square error (RMS). The results of the novel network, HNN, have shown the ability to accurately predict the percentage of volume shrinkage with the 1.02% and 4.87% error at training and testing stages, respectively and for warpage with the 3.76% and 2.47% error at training and testing stages, respectively. These accuracy results are similar to those of backpropagation neural network (BPNN), but HNN has shown the superior fast converging about 38.5% and 66.7% over than those of BPNN
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Automatic detection and staging of lung tumors using locational features and double-staged classifications
(2019-06-01)
Paing, May Phu
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Hamamoto, Kazuhiko
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Tungjitkusolmun, Supan
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Pintavirooj, Chuchart
Lung cancer is a life-threatening disease with the highest morbidity and mortality rates of any cancer worldwide. Clinical staging of lung cancer can significantly reduce the mortality rate, because effective treatment options strongly depend on the specific stage of cancer. Unfortunately, manual staging remains a challenge due to the intensive effort required. This paper presents a computer-aided diagnosis (CAD) method for detecting and staging lung cancer from computed tomography (CT) images. This CAD works in three fundamental phases: segmentation, detection, and staging. In the first phase, lung anatomical structures from the input tomography scans are segmented using gray-level thresholding. In the second, the tumor nodules inside the lungs are detected using some extracted features from the segmented tumor candidates. In the last phase, the clinical stages of the detected tumors are defined by extracting locational features. For accurate and robust predictions, our CAD applies a double-staged classification: the first is for the detection of tumors and the second is for staging. In both classification stages, five alternative classifiers, namely the Decision Tree (DT), K-nearest neighbor (KNN), Support Vector Machine (SVM), Ensemble Tree (ET), and Back Propagation Neural Network (BPNN), are applied and compared to ensure high classification performance. The average accuracy levels of 92.8% for detection and 90.6% for staging are achieved using BPNN. Experimental findings reveal that the proposed CAD method provides preferable results compared to previous methods; thus, it is applicable as a clinical diagnostic tool for lung cancer.
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Discrete wavelet transform and back-propagation neural networks algorithm for fault location on single-circuit transmission line
(2008-01-01)
Ngaopitakkul, Atthapol
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Pothisarn, Chaichan
This paper proposes a technique using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) for locating of fault location on single circuit transmission lines. The ATP/EMTP was used to simulated fault signals. The mother wavelet daubechies4 (db4) is employed to decompose, high frequency component from these signals. The first peak time in first scale of each bus that can detect fault are used as input pattern for the training pattern. It is shown that the proposed technique gives satisfactory. © 2008 IEEE.

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