Factors affect wall slip: Particle size, concentration, and temperature

Concentrated suspensions are very complex in nature and exhibit non-Newtonian flow properties although the suspending fluid might behave as a Newtonian fluid. Among the interesting properties, wall slip will be the main focus of this study. The formation of wall slip layer adjacent to the solid boundary may lead to inaccurate measurement of rheological properties. So, the measured viscosity can be lower than the actual viscosity and thus a basic understanding on wall slip is critical. Concentration, particle size, and temperature are the factors affecting the wall slip mechanism. Therefore, this research study tends to study the relationship between the parameters (concentration, particle size, and temperature) and wall slip. The result shows that the slip velocity increases with shear stress under the conditions where (i) concentration decreases, (ii) particle size increases, and (iii) temperature increases. Two regression models considering the three parameters are proposed and can be used respectively as an alternative to predict slip velocity and true shear rate

Assessing Risk and Sources of Heavy Metals in a Tropical River Basin: A Case Study of the Selangor River, Malaysia

Heavy metal concentration has become a major concern for water quality in rivers. Rapid urbanization and industrialization contribute to heavy metal concentrations in river water. This study aims to investigate the distribution, source, and environmental risk of heavy metals in the Selangor River basin in Malaysia. A total of 132 water samples were collected from 11 sampling stations on a monthly basis over a one-year period. Thirteen heavy metals were analyzed using inductively coupled plasma optical emission spectroscopy (ICP-OES). In order to identify the sources of heavy metals along the river basin, multivariate statistical techniques like principal component analysis (PCA) and cluster analysis (CA) were performed. It was found that As, Mn, and Fe exceeded the admissible limits of the Malaysian National Standard Water Quality (NSDWQ) at several of the sampling stations. Heavy metal pollution index (HPI) was below the critical pollution index value of 100. Statistical analyses showed that potential sources of heavy metals are land-based, thereby implying that former tin mining and industries in the surrounding area are the most likely sources. Anthropogenic metal concentrations were found to be low in the Selangor River, indicating that it has yet to be burdened by pollution of heavy metals

Assessment of pollution and improvement measure of water quality parameters using scenarios modeling for Sungai Selangor Basin

Sungai Selangor is very important from the viewpoint of water supply and multipurpose water use in Malaysia. The water quality of this river is degrading due to point and non-point sources of pollution. This study, focus on the water quality assessment and simulation the effect of the pollution sources from urbanization to the Sungai Selangor basin. Water quality Index (WQI) is used to define the status of river water quality and the QUAL2K was used as a simulation model. Water quality parameters DO, BOD and NH3-N have been chosen for modeling. In addition, five different model scenarios were simulated to observe the impacts of pollution sources on the Sungai Selangor water quality. WQI results showed that most of the stations in this river basin recorded water inferior to Class III. The water quality model presented different scenarios for changes of Sungai Selangor water quality. Simulation results for different scenarios showed that reduced levels of BOD and NH3-N at 51.10% and 66.18%, respectively, can be obtained if Scenario-5 is employed. The river water quality issue in the Rawang sub- basin within the study area is considered crucial to create significant improvement within the sub basin and in the downstream area of Sungai Selangor basin

Soil erosion assessment on hillslope of GCE using RUSLE model

A new method for obtaining the C factor (i.e., vegetation cover and management factor) of the RUSLE model is proposed. The method focuses on the derivation of the C factor based on the vegetation density to obtain a more reliable erosion prediction. Soil erosion that occurs on the hillslope along the highway is one of the major problems in Malaysia, which is exposed to a relatively high amount of annual rainfall due to the two different monsoon seasons. As vegetation cover is one of the important factors in the RUSLE model, a new method that accounts for a vegetation density is proposed in this study. A hillslope near the Guthrie Corridor Expressway (GCE), Malaysia, is chosen as an experimental site whereby eight square plots with the size of 8 × 8 and 5 × 5 m are set up. A vegetation density available on these plots is measured by analyzing the taken image followed by linking the C factor with the measured vegetation density using several established formulas. Finally, erosion prediction is computed based on the RUSLE model in the Geographical Information System (GIS) platform. The C factor obtained by the proposed method is compared with that of the soil erosion guideline Malaysia, thereby predicted erosion is determined by both the C values. Result shows that the C value from the proposed method varies from 0.0162 to 0.125, which is lower compared to the C value from the soil erosion guideline, i.e., 0.8. Meanwhile predicted erosion computed from the proposed C value is between 0.410 and 3.925tha-1yr-1 compared to 9.367 to 34.496tha-1yr-1 range based on the C value of 0.8. It can be concluded that the proposed method of obtaining a reasonable C value is acceptable as the computed predicted erosion is found to be classified as a very low zone, i.e. less than 10tha-1yr-1 whereas the predicted erosion based on the guideline has classified the study area as a low zone of erosion, i.e., between 10 and 50tha-1yr-1

A hybrid bat–swarm algorithm for optimizing dam and reservoir operation

One of the major challenges and difficulties to generate optimal operation rule for dam and reservoir operation are how efficient the optimization algorithm to search for the global optimal solution and the time-consume for convergence. Recently, evolutionary algorithms (EA) are used to develop optimal operation rules for dam and reservoir water systems. However, within the EA, there is a need to assume internal parameters at the initial stage of the model development, such assumption might increase the ambiguity of the model outputs. This study proposes a new hybrid optimization algorithm based on a bat algorithm (BA) and particle swarm optimization algorithm (PSOA) called the hybrid bat–swarm algorithm (HB-SA). The main idea behind this hybridization is to improve the BA by using the PSOA in parallel to replace the suboptimal solution generated by the BA. The solutions effectively speed up the convergence procedure and avoid the trapping in local optima caused by using the BA. The proposed HB-SA is validated by minimizing irrigation deficits using a multireservoir system consisting of the Golestan and Voshmgir dams in Iran. In addition, different optimization algorithms from previous studies are investigated to compare the performance of the proposed algorithm with existing algorithms for the same case study. The results showed that the proposed HB-SA algorithm can achieve minimum irrigation deficits during the examined period and outperforms the other optimization algorithms. In addition, the computational time for the convergence procedure is reduced using the HB-SA. The proposed HB-SA is successfully examined and can be generalized for several dams and reservoir systems around the world. © 2019, Springer-Verlag London Ltd., part of Springer Nature

A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems

Multi-purpose advanced systems are considered a complex problem in water resource management, and the use of data-intelligence methodologies in operating such systems provides major advantages for decision-makers. The current research is devoted to the implementation of hybrid novel meta-heuristic algorithms (e.g., the bat algorithm (BA) and particle swarm optimization (PSO) algorithm) to formulate multi-purpose systems for power production and irrigation supply. The proposed hybrid modelling method was applied for the multi-purpose reservoir system of Bhadra Dam, which is located in the state of Karnataka, India. The average monthly demand for irrigation is 142.14 (106 m3), and the amount of released water based on the new hybrid algorithm (NHA) is 141.25 (106 m3). Compared with the shark algorithm (SA), BA, weed algorithm (WA), PSO algorithm, and genetic algorithm (GA), the NHA decreased the computation time by 28%, 36%, 39%, 82%, and 88%, respectively, which represents an excellent enhancement result. The amount of released water based on the proposed hybrid method attains a more reliable index for the volumetric percentage and provides a more effective operation rule for supplying the irrigation demand. Additionally, the average demand for power production is 18.90 (106 kwh), whereas the NHA produces 18.09 (106 kwh) of power. Power production utilizing the NHA’s operation rule achieved a sufficient magnitude relative to that of stand-alone models, such as the BA, PSO, WA, SA, and GA. The excellent proficiency of the developed intelligence expert system is the result of the hybrid structure of the BA and PSO algorithm and the substitution of weaker solutions in each algorithm with better solutions from other algorithms. The main advantage of the proposed NHA is its ability to increase the diversity of solutions and hence avoid the worst possible solutions obtained using BA, that is, preventing a decrease in local optima. In addition, the NHA enhances the convergence rate obtained using the PSO algorithm. Hence, the proposed NHA as an intelligence model could contribute to providing reliable solutions for complex multi-purpose reservoir systems to optimize the operation rule for similar reservoir systems worldwide