Order Model Research Articles

Nonlinear second order plus time delay model identification and nonlinear PID controller tuning based on extended linearization method

PID control systems based on the first order plus time delay model (FOPTD), which approximate the full system dynamics, are well-accepted for a wide range of linear processes. While such controllers can be applied to overdamped nonlinear processes, they often experience excessive overshoots and oscillations for general nonlinear processes. To overcome this limitation, we propose a novel method to design a nonlinear PID controller based on the second order plus time delay (SOPTD) model. The system nonlinearity requires parameter adjustments of the linearized model across operational ranges. Hence, in this work, it is handled by the extended linearization method (ELM), ensuring local stability under the assumptions of slow and small changes in operating points. Importantly, the model achieves global input-to-output stability even without the above constraints, provided there are no structural and parametric errors. The resulting nonlinear SOPTD model can describe changes in process gain and two time constants as the operation point varies. We demonstrate the applicability of our approach with a polymerization reactor simulation and liquid-level control experiments.

Feature importance explanation of prosthetic valve endocarditis through machine learning via SHAP

Abstract Introduction Diagnosing prosthetic valve endocarditis poses a significant challenge in daily clinical practice. Accurately identifying these patients is vital due to its treatment implications. Clinical and imaging assessment of suspected cases yields a substantial number of interrelated variables, which can be analyzed through machine learning techniques in order to improve diagnostic accuracy. Feature ranking facilitates identification and interpretability of relevant clinical and imaging variables in this task. Purpose To utilize machine learning feature ranking and modeling in order to improve interpretable identification of patients with prosthetic valve endocarditis. Methods 89 cases defined by the endocarditis team and 173 controls were analyzed. We implemented the SHapley Additive exPlanations (SHAP) method to evaluate feature importance. 5-fold cross-validated Xgboost models were trained and independently tested with additive clinical, echocardiographic and 18F-FDG PET/CT data. Performance was evaluated through confusion matrices, F1-scores and AUCs. These were compared against conventional logistic regression of the Duke Criteria classification. Results Mean age was 59.6 ± 15.7, with 29.7% women. 14 clinical (demographic, laboratory and cultures), 2 echocardiographic, and 5 PET/CT features were included. The XGBoost model trained with all additive features demonstrated an AUC of 0.91±0.1 and outperformed simpler models as well as the Duke classification model (AUC=0.85). The clinical variables alone achieved an AUC of 0.85±0.11, while integration with echocardiography enhanced its performance to 0.90±0.04 (Fig. 1). Feature ranking demonstrated that antibiotic treatment duration, positive blood cultures, valve type, weight and CRP were the most relevant clinical variables, while the presence of vegetation or abscess, and abnormal uptake and uptake focality on PET/CT represented the most relevant imaging variables (Fig. 2). Conclusion Machine learning-based feature ranking and modeling may significantly enhance identification of patients with prosthetic valve endocarditis beyond standard clinical criteria. Feature selection offers interpretable performance in identifying these cases, model testing in clinical decision support may be warranted.Fig. 1:Comparative AUC Analysis.Fig. 2:SHAP feature importance.

Costs of Adjustment, Portfolio Separation, and the Dynamic Behavior of Bank Loans and Deposits – Revised

AbstractWe revise the solutions of the dynamic optimization model of Elyasiani, Kopecky, and Van Hoose (1995), and show that the optimal policy function for the case of adjustment costs and no portfolio separation is a vector autoregressive model of order 1, and not of order 2. In addition, the empirical approach of Elyasiani, Kopecky, and Van Hoose (1995) is incorrect. The optimal policy functions for the no portfolio separation case have to be estimated with a panel vector autoregression model and not with a simultaneous equation approach. We also describe how the portfolio separation hypothesis can be tested empirically based on the correct policy functions.

Study of fractional order rabies transmission model via Atangana–Baleanu derivative

In this work, we aim at disentangling the theoretical contribution through mathematical modeling approach to advance the understanding of rabies dynamics and control in livestock population. A fractional order model of rabies, using Atangana–Baleanu fractional operator is created. The analysis of suggested system and its application are both conducted. The capacity of proposed model to forecast the disease can help researchers and livestock health care agencies to take preventive actions.

Ag2O/ZnO/CuO and Ag2O/ZnO/NiO immobilised on reduced graphene oxide: synthesis, characteristics and its adsorption, catalytic behaviour and antibacterial activities

ABSTRACT Ag2O/ZnO/CuO (A/Z/C) and Ag2O/ZnO/NiO (A/Z/N) nanometal oxides were synthesised in different rations by co-precipitation method and were immobilised on reduced graphene oxide (rGO). The prepared nanocomposites were characterised by Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). Their application in the adsorption of MB, RhB and MO dyes, olefin oxidation and antibacterial properties were investigated. The results showed that A/Z/N/rGO adsorbent has 99% (10 min), 93% (20 min) and 97% (12 min) adsorption of MB, RhB and MO, respectively. From the investigation of Freundlich and Langmuir models, it was found that the adsorbent follows Langmuir. Moreover, the reaction thermodynamics was also studied, the reaction was endothermic and spontaneous. The kinetics of dye adsorption MB, RhB and MO on the surface follow a pseudo-second order model. At optimum condition, the conversion and selectivity of α-methylstyrene to acetophenone was >99% by A/Z/N and A/Z/C catalysts. A/Z/C/rGO nanocomposite has good antibacterial properties against two bacteria E. coli and S. aureus.

Viscoelastic modeling based force control framework for enhancing operation safety of soft tissue for orthopedic surgical robots

In robot-assisted laminectomy, precise control of touch force on the spinal cord during the dissection of ossified ligaments is critical to prevent dura mater tearing and ensure surgical safety. However, the complex viscoelastic characteristics of tissue at the lesion site pose a significant challenge for accurately applying touch force. This paper proposes a safe touch force servo framework based on tissue model identification and preoperative optimization of the force controller. First, a soft tissue model is established using elastic and viscoelastic elements, and the transfer function from tissue deformation to touch force is derived. Subsequently, the path and parameters of a safe pre-touch are designed, and force information required for tissue model identification is obtained through the pre-touch experiment. The model order and specific parameter values are then identified using a differential evolution algorithm. Next, force control simulation is conducted based on the identified model, and a specifically designed loss function is introduced to achieve preoperative tuning of the force controller. Finally, step force control experiments on sheep spines validate the effectiveness of the proposed method. This paper provides a safe and available touch motion control framework that can be expandable to impose precise force on various vulnerable soft tissues, with the potential for widespread application.

Model complexity optimization of equivalent dynamical linearization data models used in model‐free adaptive control

AbstractThis paper discusses a strategy for optimizing the complexity of time‐varying data models as used in model‐free adaptive control (MFAC). Here the dynamic linearization in compact form (CFDL), partial form (PFDL), and full form (FFDL) are considered as data models used to describe input/output (I/O) data sets. These data models are built only for control purpose and can have various degrees of complexity depending on the size of the considered time‐window as well as the underlying algorithms. The methodology is to compare the performance of the data models according to an evaluation criterion, to analyze the order of different data models based on bias‐variance trade‐off, and to select the best‐performing model. The complexity of the selected model is compared with the reduced linear time‐invariant (LTI) model obtained by applying a combination of the eigensystem realization algorithm (ERA) and observer/Kalman filter identification (OKID) on the same I/O dataset. The I/O data are obtained from applying the MFAC controllers on a nonlinear three‐tank system (3TS) to track various desired references. The results indicate that the model complexity optimization can also be used for selecting an appropriate MFAC data model with optimal order to reduce the complexity and computational burden of the MFAC control algorithms.

A frequency-independent absorption function surrogate for perfectly matched layer in exterior acoustics

In many engineering applications, the solution of acoustic wave problems in the infinite domain is required over a broad frequency range with densely sampled increments. In order to achieve efficient numerical simulations via a spatial discretization, e.g. finite element method, additional artificial absorbing boundaries are necessary to truncate the computational domain into appropriate bounded sizes. One of the most commonly used non-reflecting techniques to attenuate propagating waves is known as the perfectly matched layer. However, the system matrices arising from the finite element treatment of the Helmholtz equation in the absorbing layers are frequency-dependent, implying that they must be formed and inverted at each frequency of interest. Such a procedure is rather troublesome for frequency sweeps. To address this, a surrogate of perfectly matched layers is proposed, which enables the corresponding system matrices to be independent of the frequency. Moreover, it avoids the use of a relatively large computational domain and relatively thick enclosed layers at low frequencies, thus improving the ability of perfectly matched layers across the entire frequency range. After that, an adaptive projection-based model order reduction scheme is further developed to reduce the computational complexity of exterior acoustic systems. A robust error indicator based on the relative error of two constructed reduced order models is accordingly introduced. The performance of the present solution framework is discussed and compared with other implementation strategies, in the context of multi-frequency solution of two-dimensional test models with single or multiple scatterers.

High-fidelity simulations of microramp-controlled shock wave/boundary layer interaction

Microvortex generators (MVGs) are a promising solution to control shock wave/turbulent boundary layer interactions (SBLIs), especially in supersonic inlets. In this study, we examine the effects of a microramp vortex generator on an SBLI generated by an oblique shock wave and a turbulent boundary layer using direct numerical simulations (DNSs). Two cases, with and without the presence of a microramp, are compared in terms of their mean and unsteady flow features at free-stream Mach number equal to 2 and friction Reynolds number at the inviscid shock impingement equal to 600. The long integration period allows us to assess how microramps affect the typical low-frequency unsteadiness observed in SBLIs, and the data generated may serve as a reference for simulations of lower fidelity or reduced order models. The analysis shows that the three-dimensional microramp wake alters the interaction region dramatically, inducing a significant spanwise modulation and topology change of the separation. For example, tornado-like structures redistribute the flow in both the spanwise and wall-normal directions inside the recirculation region. The increase in momentum close to the wall by the ramp vortices effectively delays the onset of the separation and, thus, the separation length, but at the same time leads to a significant increase in the intensity of the wall-pressure fluctuations. We then characterise the mutual interaction between the arch-like vortices around the ramp wake and the SBLI. The specific spanwise vorticity shows that these vortices follow the edge of the separation and their intensity, apart from mean compressibility effects, is not affected by the shocks. The shocks, instead, are deformed in shape by the periodic impingement of the vortices, although the spectral analysis did not reveal any significant trace of their shedding frequency in the separation region. These Kelvin–Helmholtz vortices, however, may be relevant in the closure of the separation bubble. Fourier analysis also shows a constant increase, in both value and magnitude, in the low-frequency peak all along the span, suggesting that the motion of the separation shock remains coherent while being disturbed by the arch-like vortices and oscillating at a higher frequency in absolute terms.

Capital structure of special-purpose governments

PurposeThe paper examines whether special purpose governments follow the pecking order model when raising capital, replacing firm equity in the original model with local intergovernmental revenues. Special purpose governments are a relatively underexamined component of state and local governments, and their capital structure choices have important implications for America’s aggregate fiscal health. This paper seeks to illuminate special purpose governments’ choices among available forms of capital.Design/methodology/approachTo address our research inquiry regarding whether special-purpose governments adhere to the pecking order theory, we narrow our focus to a specific group of transit entities. Our investigation utilizes data sourced from two distinct repositories: the United States Census of Governments and the National Transit Database. To facilitate integration of these disparate datasets, we establish a correspondence between them using the Federal Information Processing Standard and the transit identification number. Initially, our analysis employs maximum likelihood estimation, comparing these estimates to those generated by a naïve model. Subsequently, we derive parameter estimates through the application of a bivariate probit model.FindingsThe paper supports the pecking order hypothesis where internal funds are consumed first, debt is consumed next and IGR is consumed last. The results are robust and are not influenced by simultaneity bias.Research limitations/implicationsThe paper uses transit special purpose governments as the special purpose government of interest. These transit special purpose governments have high fixed costs that may inform their capital structure decisions relative to non-transit special purpose governments. Additionally, transit special purpose governments often have a profit-maximizing objective that may not uniformly apply to other special purpose governments, such as school districts, who lack such an incentive.Practical implicationsOur research should grab the attention of state and local politicians, voters, policy experts and scholars. Special-purpose governments, a key part of our governmental system, are on the rise. Understanding them better can guide decision-making on how to allocate resources, set policies and plan strategically. This knowledge boosts financial reporting quality, transparency, accountability and public confidence. Journalists can leverage our findings to ensure accurate and thorough reporting, fostering accountability and trust. Additionally, debt markets and analysts can factor this information into their risk assessments.Originality/valueThe paper enhances our understanding of how special purpose governments interact with existing models of corporate finance when making capital structure decisions.

Mathematical modelling, energy consumption, and quality evaluation of wheat seeds subjected to intermittent drying

AbstractThis work aims to evaluate the kinetic profile of intermittent drying of wheat seeds using traditional models from the literature and the fractional calculus technique. Furthermore, it aims to verify the application of the intermittent drying process on the amount of antioxidant compounds, protein, and lipid content of the grain, in addition to energy consumption to obtain the desired final moisture content of the wheat. It was verified that the prediction of drying kinetics by Page and fraction order models were similar (modelling efficiency varying between the range of 0.917–0.995, varying the drying condition and wheat cultivar). Regarding antioxidant compounds for the three wheat cultivars, it can be seen that the higher fraction of ethanol (74.0% and 90.36%) used for extraction had greater process efficiency. Regarding the protein content in the three wheat cultivars, lower drying temperatures and intermittency periods result in lower quality losses of material (12.3% for BRS‐Atobá wheat, 9.89% for BRS‐Jacana wheat, and 18.71% for BRS‐Sanhaço wheat). In terms of lipids, it was found that the influence of temperature was greater on the lipid content than on the protein content of the material (for the best drying condition, there were percentage decreases for the best condition of 36.16% for the BRS‐Atobá wheat, 34.9% for BRS‐Jacana, and 52.2% for BRS‐Sanhaço). Regarding energy consumption during the drying process, it can be seen that the conditions used for intermittency and drying time, in addition to the sample conditions, directly impact energy consumption.

Calibration-Based ALE Model Order Reduction for Hyperbolic Problems with Self-Similar Travelling Discontinuities

We propose a novel Model Order Reduction framework that is able to handle solutions of hyperbolic problems characterized by multiple travelling discontinuities. By means of an optimization based approach, we introduce suitable calibration maps that allow us to transform the original solution manifold into a lower dimensional one. The novelty of the methodology is represented by the fact that the optimization process does not require the knowledge of the discontinuities location. The optimization can be carried out simply by choosing some reference control points, thus avoiding the use of some implicit shock tracking techniques, which would translate into an increased computational effort during the offline phase. In the online phase, we rely on a non-intrusive approach, where the coefficients of the projection of the reduced order solution onto the reduced space are recovered by means of an Artificial Neural Network. To validate the methodology, we present numerical results for the 1D Sod shock tube problem, for the 2D double Mach reflection problem, also in the parametric case, and for the triple point problem.

Topic modelling and sentiment analysis during COVID-19 revealed emotions changes for public health

Between 2020 and 2022, lockdown and restraining measures as a result of the COVID-19 pandemic led to changes in daily routines and also in language usage. At certain points during the pandemic, populations in some countries were unwilling or unlikely to respond to government messages, either because of the tone and analytic discourse used by leaders, or because they did not understand the messages. Linguistic markers and meanings were therefore linked to low levels of engagement, negative emotions and high levels of analytical thinking, especially in relation to the discourses of influential international leaders. We subjected sixteen speeches by eight country leaders to topic modelling and sentiment analysis in order to understand how the psychological functions of language were affected during two different periods of the COVID-19 pandemic. In this topic analysis we organize 39,073 words collected from sixteen authentic speeches delivered in two different periods of the acute phase of the pandemic. These were encoded in the Linguistic Inquiry Word Count program (LIWC), with the main aim being to identify differences between the periods. We examined the following aspects: (1) the emotional tone, analytical thinking and clout (empathy dimension); (2) the changes in these three dimensions or factors between periods 1 and 2 (February and May 2020). We observed a negative relationship between emotional tone and analytical thinking and a positive relationship between clout and emotional tone. When we considered the changes in pandemic circumstances, the psycholinguistic profiles of eight country leaders demonstrated fluctuations in language and emotions. Further reviews and research should focus on the current language and deficit wording of this population (leaders). We also note that psychologists and schoolteachers can play an important role in supporting language programmes with positive wording and by emphasising the collateral effects of face-to-face classes when teaching children to read and write.

Point-enhanced convolutional neural network: A novel deep learning method for transonic wall-bounded flows

Low order models can be used to accelerate engineering design processes. Ideally, these surrogates should meet the conflicting requirements of large design space coverage, high accuracy and fast evaluation. Within the context of aerospace applications at transonic conditions, this can be challenging due to the associated non-linearity of the flow regime. Different methods have been investigated in the past to predict the flow-field around shapes such as airfoils or cylinders. However, they usually have reduced spatial resolution, limiting the prediction capabilities within the boundary layer which is of interest for transonic wall-bounded flows. This work proposes a novel Point-Enhanced Convolutional Neural Network (PCNN) method that combines the advantages of the well-established PointNet and convolutional neural network approaches. The PCNN model has relatively low memory requirements in the training process, preserves the spatial correlation in the domain and has the same resolution as a traditional computational method. The architecture is used for the flow-field prediction of civil aero-engine nacelles in which it is demonstrated that the flow features of peak isentropic Mach number (Mis), pre-shock isentropic Mach number and shock location (X/Lnac) are captured within ΔMis = 0.02, ΔMis=0.04, ΔX/Lnac=0.007, respectively. The PCNN model successfully predicts the integral parameters of the boundary layer, in which the incompressible displacement thickness, momentum thickness and shape factor are typically within 5% of the CFD. Overall, the PCNN method is demonstrated for transonic wall-bounded flows for a range of flow physics that include shock waves and shock-induced separation.

Nonlinear vibrations and static bifurcation of a hyperelastic pipe conveying fluid

This study investigates the free and forced nonlinear vibrations, along with the static bifurcation behavior, of a fluid transmission hyperelastic pipe supported by clamped ends. Both geometrical and material nonlinearities are taken into account. The mathematical model is grounded in Euler–Bernoulli beam theory, incorporating von Kármán nonlinearity to capture the complexities of the system. For the hyperelastic materials, the strain energy function proposed by Yeoh is employed. Considering that the lowest critical velocity of the fluid flow is associated with the first mode, after deriving the governing equation using Hamilton’s principle, Galerkin discretization is applied to obtain the reduced order model in the first mode. Analysis of static bifurcation and stability is performed and critical velocity values of fluid flow are determined. The analysis of free and forced vibrations of the hyperelastic pipe is conducted using the method of multiple time scales, specifically focusing on the sub-critical velocity range of fluid flow, which corresponds to the unbuckled state of the pipe. This approach allows for a comprehensive examination of how fluid flow velocity influences the frequency characteristics of free vibrations, as well as the frequency response in the vicinity of the main resonance. The results obtained from this study can significantly enhance our understanding of the design and application of hyperelastic pipes in various fluid transfer scenarios. By elucidating the dynamic behavior and critical parameters associated with these pipes, the findings can inform engineering practices and contribute to the optimization of systems that utilize hyperelastic materials for fluid conveyance.

Low‐rank approximations of frequency‐limited discrete‐time Gramians

AbstractWe consider the approximate computation of Gramians of large‐scale linear discrete‐time state‐space systems. Of particular interest are the Gramians which arise in frequency‐limited balanced truncation model order reduction. While there are many results for the continuous‐time situation, the discrete‐time scenario is usually considered to a much lesser extent. The Gramians are solutions of discrete‐time Lyapunov matrix equations (also called Stein matrix equations) which, if the reduction is restricted to smaller frequency intervals, also incorporate matrix functions in the form of matrix logarithms. We discuss the efficient numerical handling of these matrix function as well as the computation of low‐rank Gramian approximation by rational Krylov subspace and related iterative methods.

Quantitative Macromolecular Modeling Assay of Biopolymer-Based Hydrogels

The rubber elasticity theory has been lengthily applied to several polymeric hydrogel substances and upgraded from idealistic models to consider imperfections in the polymer network. The theory relies solely on hyperelastic material models in order to provide a description of the elastic polymer network. While this is also applicable to polymer gels, such hydrogels are rather characterized by their water content and visco-elastic mechanical properties. In this work, we applied rubber elasticity constitutive models through hyperelastic parameter identification of hydrogels based on their stress–strain response to compression. We further performed swelling experiments and determined the intrinsic properties, i.e., density, of the specimens and their components. Additionally, we estimated their equilibrium swelling and employed it in the swelling-equilibrium theory in order to determine the polymer–solvent interaction parameter of each hydrogel with regard to cross-linking. Our results show that the average mesh size obtained from the rubber elasticity theory can be regarded as a concentration-dependent characteristic length of the hydrogel’s network and couples the non-linear elastic response to the specimens’ inherent visco-elasticity through hysteresis as a quantifier of energy dissipation under large deformation.

Nicorandil treatment improves survival and spatial learning in aged granulin knockout mice.

Mutations in the human granulin (GRN) gene are associated with multiple diseases, including dementia disorders such as frontotemporal dementia (FTD) and limbic-predominant age-related TDP-43 encephalopathy (LATE). We studied a Grn knockout (Grn-KO) mouse model in order to evaluate a potential therapeutic strategy for these diseases using nicorandil, a commercially available agonist for the ABCC9/Abcc9-encoded regulatory subunit of the "K+ATP" channel that is well-tolerated in humans. Aged (13 months) Grn-KO and wild-type (WT) mice were treated as controls or with nicorandil (15 mg/kg/day) in drinking water for 7 months, then tested for neurobehavioral performance, neuropathology, and gene expression. Mortality was significantly higher for aged Grn-KO mice (particularly females), but there was a conspicuous improvement in survival for both sexes treated with nicorandil. Grn-KO mice performed worse on some cognitive tests than WT mice, but Morris Water Maze performance was improved with nicorandil treatment. Neuropathologically, Grn-KO mice had significantly increased levels of glial fibrillary acidic protein (GFAP)-immunoreactive astrocytosis but not ionized calcium binding adaptor molecule 1 (IBA-1)-immunoreactive microgliosis, indicating cell-specific inflammation in the brain. Expression of several astrocyte-enriched genes, including Gfap, were also elevated in the Grn-KO brain. Nicorandil treatment was associated with a subtle shift in a subset of detected brain transcript levels, mostly related to attenuated inflammatory markers. Nicorandil treatment improved survival outcomes, cognition, and inflammation in aged Grn-KO mice.

Innovative eco-friendly methyl orange removal: Mechanism, kinetic, and thermodynamic study using starch cryogel-integrated mesoporous silica nanoparticles

This study introduces a novel, eco−friendly composite, uncalcined mesoporous silica nanoparticles incorporated into a starch cryogel (MSNs-Cry), designed for the effective removal of methyl orange (MO) from water. MSNs−Cry integrates uncalcined mesoporous silica nanoparticles (MSNs) within a starch cryogel network, leveraging the high adsorption capacity of MSNs. The composite achieved a maximum adsorption capacity of 18.98 mg g⁻1 and demonstrated high removal efficiencies of 99.00 % ± 0.21 % in synthetic water (10 mg L−1 MO) and 92.77 % ± 1.76 % in real wastewater containing 0.43 mg L−1 MO. The Langmuir isotherm model provided a superior fit (R2 = 0.9930) compared to the Freundlich model (R2 = 0.9180), and the adsorption kinetics followed a pseudo−second−order model (R2 = 0.9917). The primary adsorption mechanisms included electrostatic attraction, hydrophobic interactions, and hydrogen bonding. The process was endothermic (ΔH° = 31.3 kJ mol−1), spontaneous, and more favorable at higher temperatures (ΔG° = −34.2 to −38.6 kJ mol−1 at 298–318 K). In the presence of sodium silicate at 13.1 times the MO concentration, removal efficiency drops by 35.77 %, and with sodium sulfate and urea at 100 times the MO concentration, it decreases by 8.65 %. Despite these challenges, MSNs−Cry effectively removes MO in the presence of the anionic dye Congo Red and metal ions, demonstrating its selective adsorption capabilities. The tablet form of MSNs−Cry prevents the loss of uncalcined MSNs, mitigating potential environmental and operational impacts. Additionally, the composite's effectiveness at a natural pH of 6.65 eliminates the need for pH adjustment, offering a cost−effective solution for real−world applications. This study establishes MSNs−Cry as a promising material for sustainable water purification.

“Chacco” clay from the Peruvian highlands as a potential adsorbent of heavy metals in water

This research aimed to remove Cd (II), Cr (VI), Ni (II), Pb (II), and V (V) from aqueous solutions prepared in distilled water using “Chacco” clay from the Peruvian highlands as adsorbent. The adsorption process was carried out in Batch type systems for 120 min using solutions of each metal at a concentration of 5 mg/L in aqueous systems of a single metal or monometallic (MAS) and solutions of the five metals simultaneously or multimetallic (MMAS). For this purpose, the “Chacco” clay was first characterized by SEM-EDS analysis, finding a laminated clay structure with an elemental composition of C, Al, Fe, Na, Mg, Ca, K, As, Cu, Pd, O, and Ta. The results using 10 g/L of “Chacco” clay showed that the best adsorption efficiency in both MAS and MMAS aqueous systems is achieved at pH = 4 achieving in MAS aqueous systems the removal of 64.16 ± 0.98 % of Cd (II), 95.70 ± 0.81 % of Cr (VI), 97.20 ± 0.89 % of Ni (II), 92. 78 ± 0.79 % of Pb (II), and 95.80 ± 0.67 % of V (V), on the other hand, in aqueous MMAS systems a decrease in adsorption efficiency was observed, managing to remove 6.88 ± 0.53 % of Cd (II), 63.04 ± 0.94 of Cr (VI), 7.81 ± 0.43 % of Ni (II), 62.34 ± 0.77 % Pb (II), and 14.33 ± 0.56 % of V (V). The kinetic study showed that the adsorption mechanism would correspond to chemisorption since the process fitted best to the pseudo-second order model and Elovich. SEM-EDS analysis after adsorption confirmed the presence of the heavy metals under study in the “Chacco” clay. Metal adsorption is evidenced at 1418 cm−1 by -CH2-metal deformation vibrations according to FTIR analysis. In conclusion, the “Chacco” clay would be a promising adsorbent of heavy metals in polluted waters so that scaling up to real environments could be feasible. On the other hand, the “Chacco” clay is consumed by the population of Puno, Peru, therefore its potential impact on health should be evaluated due to its capacity to accumulate metals and the presence of Al in this clay.