Use of Radial Basis Function Network to Predict Optimum Calcium and Magnesium Levels in Seawater and Application of Pretreated Seawater by Biomineralization as Crucial Tools to Improve Copper Tailings Flocculation

Villca, Grecia; Arias, Dayana; Jeldres, Ricardo I.; Pánico, Antonio; Rivas, Mariella; Cisternas, Luís A.

doi:10.3390/min10080676

Cited by 5 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step 5: Calculate the final energy that corresponds to the value of S induced i by using Equations (15) and (16). Verify the energy by using the following condition:…”

Section: Energy Based K Satisfiability Reverse Analysis Methods (Eksatra)mentioning

confidence: 99%

“…Radial basis neural network (RBFNN) is a variant of multilayer feedforward ANN that can be explored in various application due to the forecasting capability in several works. Villca et al [ 16 ] utilized a radial basis function neural network (RBFNN) in predicting the optimum chemical composition during mining processes especially in copper tailings flocculation processes. Mansor et al [ 17 ] incorporates Boolean 2 satisfiability logical representation into RBFNN by obtaining the special parameters such as width and centre.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation

Mohd Jamaludin,

Mohd Kasihmuddin,

Md Ismail

et al. 2020

Entropy

View full text Add to dashboard Cite

An effective recruitment evaluation plays an important role in the success of companies, industries and institutions. In order to obtain insight on the relationship between factors contributing to systematic recruitment, the artificial neural network and logic mining approach can be adopted as a data extraction model. In this work, an energy based k satisfiability reverse analysis incorporating a Hopfield neural network is proposed to extract the relationship between the factors in an electronic (E) recruitment data set. The attributes of E recruitment data set are represented in the form of k satisfiability logical representation. We proposed the logical representation to 2-satisfiability and 3-satisfiability representation, which are regarded as a systematic logical representation. The E recruitment data set is obtained from an insurance agency in Malaysia, with the aim of extracting the relationship of dominant attributes that contribute to positive recruitment among the potential candidates. Thus, our approach is evaluated according to correctness, robustness and accuracy of the induced logic obtained, corresponding to the E recruitment data. According to the experimental simulations with different number of neurons, the findings indicated the effectiveness and robustness of energy based k satisfiability reverse analysis with Hopfield neural network in extracting the dominant attributes toward positive recruitment in the insurance agency in Malaysia.

show abstract

“…Step 5: Calculate the final energy that corresponds to the value of S induced i by using Equations (15) and (16). Verify the energy by using the following condition:…”

Section: Energy Based K Satisfiability Reverse Analysis Methods (Eksatra)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation

Mohd Jamaludin,

Mohd Kasihmuddin,

Md Ismail

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

海水中产生的水滑石用于修复酸性金属矿井排水和海底安置的评估

2023

View full text Add to dashboard Cite

Uncontrolled release of acid mine drainage (AMD) causes widespread detrimental impacts on the receiving environment. Thus, effective treatment to neutralise AMD effluent pH and capture a suite of metals is required. In-situ hydrotalcite (HTC) precipitation is an emerging technology for AMD remediation. HTC has an inherent capacity to accommodate a range of cations and anions during in situ formation, offering a method of broad-spectrum contaminant removal. This study explored the feasibility of using seawater as an Mg source and synthetic AMD in HTC formation. The HTC was formed from a stoichiometric combination of synthetic AMD and seawater. While three initial stoichiometric M2+:M3+ ratios of 2:1, 3:1, and 4:1 were investigated, only HTC with an M2+:M3+ ratio of 2:1 was generated, as confirmed by both mineralogical and geochemical analyses. Importantly, the HTC was demonstrated to effectively remove a suite of metals present in AMD such as Cu, Zn, Al, and Mn with removal rates of between 99.97 to 99.99%. The HTC precipitate contained ≈6.6% Cu and 4.1% Zn, and thus shows the potential, if required, for future metal recovery. Since submarine placement is often used in metal mining and processing operations proximal to the coast, the stability of the HTC precipitate in seawater was also investigated. Importantly, only 0.2% of the Cu and 1.1% of the Zn within the HTC were subsequently leaching in decreasing increments into seawater over 30 days with decreasing increments after the initial seven days. This indicates robust element retention and confirms the potential of HTC for AMD remediation with direct submarine placement.

show abstract