TỐI ƯU HÓA QUÁ TRÌNH HẤP PHỤ XANH METHYLENE BẰNG PHƯƠNG PHÁP MẠNG THẦN KINH NHÂN TẠO VÀ PHƯƠNG PHÁP MÁY VECTƠ HỖ TRỢ BÌNH PHƯƠNG NHỎ NHẤT

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Tác giả: Cường Hoàng Lương, Hảo Nguyễn Đức, Võ Hoa Sơn

Ngôn ngữ: vie

Ký hiệu phân loại:

Thông tin xuất bản: Tạp chí Khoa học - Đại học Thủ Dầu Một, 2024

Mô tả vật lý: tr.35-46

Bộ sưu tập: Metadata

ID: 445531

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The water pollution problem is becoming increasingly serious. However, the wastewater treatment process has not been carried out effectively causing a large amount of toxic substances being discharged directly into the water environment without treatment. Methylene Blue (MB) is an organic dye, widely used in many different fields such as medicine, biology, chemistry and industry. Using MB in large concentrations can cause significant health issues such as heart damage, vomiting, shock, and limb paralysis. This research aims to optimize the adsorption process for MB using a composite of Zinc Sulfate Nanoparticles and Activated Carbon (ZnS NPs/AC). We utilized Artificial Neural Networks (ANN) and Least Squares Support Vector Machine (LS-SVM) models to identify the optimal conditions for MB adsorption. The performance of the models was assessed by their determination coefficients (R²) and root mean square errors (RMSE). Results revealed that the LS-SVM model, with an R² of 0.99 and RMSE of 0.24, outperformed the ANN model, which had an R² of 0.98 and an RMSE of 0.74. The optimal adsorption conditions were achieved at a pH of 6.6, MB concentration of 8.8mg/L, adsorbent mass of 0.015g, and ultrasonication time of 4.9 minutes, yielding an adsorption efficiency exceeding 97%.Vấn đề ô nhiễm nguồn nước đang trở nên ngày càng nghiêm trọng. Tuy nhiên, quá trình xử lý nước thải chưa được thực hiện một cách hiệu quả, dẫn đến việc một lượng lớn các chất độc hại bị thải trực tiếp ra môi trường nước mà không qua xử lý. Methylene Blue (MB) là một chất nhuộm hữu cơ, được sử dụng rộng rãi trong nhiều lĩnh vực khác nhau như y học, sinh học, hóa học và công nghiệp, việc sử dụng MB với nồng độ lớn có thể gây tác động tiêu cực đối với sức khỏe, bao gồm các vấn đề như tổn thương tim, nôn mửa, sốc, và tê liệt cơ tứ chi. Trong bài báo này, chúng tôi đề xuất việc tối ưu hóa quá trình hấp phụ chất màu hữu cơ độc hại Methylene Blue (MB) nhờ vật liệu tổ hợp nano Sunfat Kẽm/Than hoạt tính ( ZnS NPs/AC ) nhằm xác định các điều kiện thực nghiệm để tối ưu hóa hiệu suất hấp phụ MB bằng mô hình trí tuệ nhân tạo (ANN) và mô hình Least Squares Support Vector Machine (LS-SVM). Kết quả thu được hệ số xác định (R2), sai số căn quân phương (RMSE) lần lượt là 0.98, 0.74 với mô hình ANN và 0.99, 0.24 với mô hình LS-SVM. Điều này cho thấy, mô hình LS-SVM cho khả năng dự báo chính xác hơn so với mô hình ANN và nhận được các điều kiện thực nghiệm tối ưu với độ pH là 6.6, nồng độ MB 8.8mg/L, khối lượng chất hấp phụ là 0.015g, thời gian siêu âm 4.9 phút và hiệu suất hấp phụ MB trên 97%.

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