Modeling Changes in the Composition of River Water with Discharged Wastewater: A Case Study in NW Russia

The technogenic impact of the development of the Lomonosov diamond deposit is associated with the discharge of quarry and drainage water into the river, which has a special conservation status. Earlier studies on the composition of bottom sediments showed that there are signs of increased accumulation of heavy metals and radionuclides at wastewater discharge sites. The purpose of this work was to predict changes in the composition of surface water and bottom sediment in the river during the further development of mining operations with brackish and salty water captured by drainage systems, the presence of which was established in the zone of their future influence. For this, a simulation of changes in the composition of the water in the river was carried out using the GEOCHEQ software package by minimizing the free energy of the system using a convex simplex algorithm. It was found that the maximum salinity of surface water can reach 1.51 g/L. In this case, the MPC of Cl−, Na+, SO42−, Mg2+, Sr, V, and U can be exceeded for fishery watercourses. The genetic basis of the accumulation of these components in solutions for mixing was considered. According to the calculations, when about 5000 m3/h of drainage water is discharge d into the river, the mass of precipitated chemical elements will be 56–191 t/h, including up to 2.1 t/h of iron; therefore, accumulation in the discharge zone must be controlled.

Optimization of a complex hydropower development operation using HEC-ResSim

In this paper HEC-ResSim is applied for a complex hydropower development formed by five reservoirs and related hydropower plants. There were considered characteristics of five existing hydropower developments in Romania, for which three reservoirs are with annual regulation and two with daily regulation. The objective function was the realization of a planned energy generation for one year (the mean hydrological year). Obtained results are very close to those obtained applying linear programming, a revised simplex algorithm.

3D crack identification using the Nelder-Mead Simplex algorithm combined with a random generation of crack positions

In this paper, we present a scheme for cracks identification in three-dimensional linear elastic mechanical components. The scheme uses a boundary element method for solving the forward problem and the Nelder-Mead simplex numerical optimization algorithm coupled with a low discrepancy sequence in order to identify an embedded crack. The crack detection process is achieved through minimizing an objective function defined as the difference between measured strains and computed ones, at some specific sensors on the domain boundaries. Through the optimization procedure, the crack surface is modelled by geometrical parameters, which serve as identity variables. Numerical simulations are conducted to determine the identity parameters of an embedded elliptical crack, with measures randomly perturbed and the residual norm regularized in order to provide an efficient and numerically stable solution to measurement noise. The accuracy of this method is investigated in the identification of cracks over two examples. Through the treated examples, we showed that the method exhibits good stability with respect to measurement noise and convergent results could be achieved without restrictions on the selected initial values of the crack parameters.

Sequence based prediction of protein phase separation into disordered condensates using machine learning

Several proteins which are responsible for neuro-degenrerative disorders (Alzheimers, Parkinsons etc) are shown to undergo a mechanism known as liquid liquid phase separation (LLPS). We in this research build a predictor which would answer whether a protein molecule would undergo LLPS or not. For this we used some protein sequences for which we already knew the answer. The ones who undergo LLPS were considered as the positive set and the ones who do not, were taken as the negative set. Depending on the knowledge of amino-acid sequences we identified some relevant variables in the context of LLPS e.g. number of amino acids, length of the best pairings, average register shifts. Using these variables we built a number of scoring functions which were basically analytic functions involving these variables and we also combined some scores already existing in the literature. We considered a total of 43636 protein sequences, among them only 121 were positive. We applied logistic regression and performed cross validation, where 25% of the data were used as the training set and the performance of the obtained results were tested on the remaining 75% of the data. In the training process, we used Simplex algorithm to maximize area under the curve (AUC) in receiver operator characteristics (ROC) space for each of the scores we defined. The optimised parameters were then used to evaluate AUC on the test set to check the accuracy. The best performing score was identified as the predicting model to answer the question whether a protein chain would undergo phase separating behavior or not.

Computing on Vertices in Data Mining

The main challenges in data mining are related to large, multi-dimensional data sets. There is a need to develop algorithms that are precise and efficient enough to deal with big data problems. The Simplex algorithm from linear programming can be seen as an example of a successful big data problem solving tool. According to the fundamental theorem of linear programming the solution of the optimization problem can found in one of the vertices in the parameter space. The basis exchange algorithms also search for the optimal solution among finite number of the vertices in the parameter space. Basis exchange algorithms enable the design of complex layers of classifiers or predictive models based on a small number of multivariate data vectors.

Analysis and Evaluation of CDPR Cable Sagging Based on ANFIS

The cable sagging problem of cable-driven parallel robots (CDPRs) is very complicated, because several models for calculating cable sag based on the well-known catenary equation have been studied, but time and computational efficiency are a problem to be solved. There is still no simple mathematical model to calculate cable sag by considering all relevant conditions due to the complexity and nonlinearity of the cable sagging model, which involves many dominant variables and their influence on the position accuracy of CDPRs. In this study, we proposed an ANFIS (adaptive neuro-fuzzy inference system) architecture to estimate cable sag for large-sized CDPRs. The ANFIS model can be used to solve nonlinear functions and detect nonlinear factors online in the control system; this characteristic is consistent with the nonlinear model of cable sag. The trained data for ANFIS models were taken from calculation results by Trust-Region-Dogleg algorithm based on two cable tension calculation algorithms as Dual Simplex Algorithm and Force Distribution in Closed Form. Cable sagging data obtained from ANFIS and Trust-Region-Dogleg algorithm are compared and evaluated by statistical factors of evaluations consisting of root-mean-square error, correlation coefficients, and scatter index. The analytical results show that the ANFIS gave computed results with small errors and can be applied to predict cable sagging for any CDPR configuration, with the advantage of fast calculation time and high precision. The results of these models are also applied on a CDPR that contains two redundant actuators.

The Optimum Allocation For Irrigation Water of Agricultural Crops in Iraq During The Average Period (2017-2020) Using Linear Programming Method

The research aims to study the optimal allocation of irrigation water that is used to irrigate various agricultural crops at the level of Iraq. In order to achieve the research aim, two economic models were formulated according to the Simplex Algorithm. The two models included forty agricultural crops, which were restricted by twenty specific production resources. The estimated results indicated that there is a surplus of the water resource for both the actual crop composition plan and the proposed basic plans for the two estimated models amounting to about 30.943, 35.357 and 31.097 billion cubic meters for each plan, respectively, compared to the quantities of water available for agricultural use. The results of the analysis of the two estimated models indicated to prefer the results of plans with legislative restrictions due to the expansion of the area of strategic and important crops for local consumption, with less needs of water.

A Method for Assessing the Impact of Changes in Demand for Coal on the Structure of Coal Grades Produced by Mines

Due to the withdrawal of coal from power generation in the EU, mining companies in Poland are forced to adapt their production to the decreasing demand. Forecasting the volume of demand plays an important role in planning the volume of the mine’s output. The demand for coal is constantly changing, with a downward trend. This article presents a method that allows to assess the impact of the variable demand on mine profits and on the volumes of sales of individual coal grades. The proposed method is based on the Monte Carlo simulation and on a solution consisting of the optimization of the production and sales of coal by the mining company (the SIMPLEX algorithm). By using the Monte Carlo simulation to forecast the demand, unlike other commonly used methods, a sufficiently large set of real situations that may occur in the future can be obtained. The results allow us to conclude the extent of desirable adjustment of the structure of the mine’s production to the requirements of its consumers, as well as to predict in which direction these changes will proceed and with what probability. The usefulness of the developed method has been verified on the example of an existing hard coal mine.