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Using Complete Coverage Path Planning (CCPP), a cleaning robot could visit every accessible area in the workspace. The dynamic environment requires the higher computation of the CCPP algorithm because the path needs to be replanned when the path might become invalid. In previous CCPP methods, when the neighbours of the current position are obstacles or have been visited, it is challenging for the robot to escape from the deadlocks with the least extra time cost. In this study, a novel CCPP algorithm is proposed to deal with deadlock problems in a dynamic environment. A priority template inspired by the short memory model could reduce the number of deadlocks by giving the priority of directions. Simultaneously, a global backtracking mechanism guides the robot to move to the next unvisited area quickly, taking the use of the explored global environmental information. What's more, the authors extend their CCPP algorithm to a multi-robot system with a market-based bidding process which could deploy the coverage time. Experiments of apartment-like scenes show that the authors’ proposed algorithm can guarantee an efficient collision-free coverage in dynamic environments. The proposed method performs better than related approaches on coverage rate and overlap length.

This work aims to model the combined cycle power plant (CCPP) using different algorithms. The algorithms used are Ridge, Linear regressor (LR), and upport vector regressor (SVR). The CCPP energy output data collected as a factor of thermal input variables, mainly exhaust vacuum, ambient temperature, relative humidity, and ambient pressure. Initially, the Ridge algorithm-based modeling is performed in detail, and then SVR-based LR, named as SVR (LR), SVR-based radial basis function—SVR (RBF), and SVR-based polynomial regression—SVR (Poly.) algorithms, are applied. Mean absolute error (MAE), R-squared (R2), median absolute error (MeAE), mean absolute percentage error (MAPE), and mean Poisson deviance (MPD) are assessed after their training and testing of each algorithm. From the modeling of energy output data, it is seen that SVR (RBF) is the most suitable in providing very close predictions compared to other algorithms. SVR (RBF) training R2 obtained is 0.98 while all others were 0.9–0.92. The testing predictions made by SVR (RBF), Ridge, and RidgeCV are nearly the same, i.e., R2 is 0.92. It is concluded that these algorithms are suitable for predicting sensitive output energy data of a CCPP depending on thermal input variables.

The integration of power plants and desalination systems has attracted increasing attention over the past few years as an effective solution to tackle sustainable development and climate change issues. In this light, this paper introduces a novel modelling and optimization approach for a combined-cycle power plant (CCPP) integrated with reverse osmosis (RO) and multi-effect distillation (MED) desalination systems. The integrated CCPP and RO–MED desalination system is thermodynamically modelled utilizing MATLAB and EES software environments, and the results are validated via Thermoflex software simulations. Comprehensive energy, exergic, exergoeconomic, and exergoenvironmental (4E) analyses are performed to assess the performance of the integrated system. Furthermore, a new multi-objective water cycle algorithm (MOWCA) is implemented to optimize the main performance parameters of the integrated system. Finally, a real-world case study is performed based on Iran's Shahid Salimi Neka power plant. The results reveal that the system exergy efficiency is increased from 8.4 to 51.1% through the proposed MOWCA approach, and the energy and freshwater costs are reduced by 8.4% and 29.4%, respectively. The latter results correspond to an environmental impact reduction of 14.2% and 33.5%. Hence, the objective functions are improved from all exergic, exergoeconomic, and exergoenvironmental perspectives, proving the approach to be a valuable tool towards implementing more sustainable combined power plants and desalination systems.

An Autonomous Underwater Helicopter (AUH) is a disk-shaped, multi-propelled Autonomous Underwater Vehicle (AUV), which is intended to work autonomously in underwater environments. The near-bottom area sweep in unknown environments is a typical application scenario, in which the complete coverage path planning (CCPP) is essential for AUH. A complete coverage path planning approach for AUH with a single beam echo sounder, including the initial path planning and online local collision avoidance strategy, is proposed. First, the initial path is planned using boustrophedon motion. Based on its mobility, a multi-dimensional obstacle sensing method is designed with a single beam range sonar mounted on the AUH. The VFH+ algorithm is configured for the heading decision-making procedure before encountering obstacles, based on their range information at a fixed position. The online local obstacle avoidance procedure is simulated and analyzed with variations of the desired heading direction and corresponding polar histograms. Finally, several simulation cases are set up, simulated and compared by analyzing the heading decision in front of different obstacle situations. The simulation results demonstrate the feasibility of the complete coverage path planning approach proposed, which proves that AUH completing a full coverage area sweep in unknown environments with a single beam sonar is viable.

(1) Background: To enhance the efficiency and minimize the energy consumption of combined cycle power plants (CCPPs), it is crucial to research gas–steam combined cycle (GSCC) performance prediction under various conditions. However, current studies focus more on the subsystems of GSCC, including simpler systems like gas turbines and steam turbines, lacking an overall perspective on the GSCC system as a whole. (2) Methods: this paper focuses on GSCC efficiency prediction, while employing continuous and fluctuating operational data from a CCPP. Specifically, variables from two symmetric gas turbines of the GSCC were employed as model inputs. Deep Neural Network, Simple Recurrent Neural Network, Long Short-Term Memory, and Gated Recurrent Unit (GRU) were tested. Furthermore, the GRU network was employed to evaluate the Plate Heat Exchanger (PHE) installation modification of the CCPP. (3) Results: GRU outperformed the other models, achieving a Mean Absolute Percentage Error of 0.855%. Utilizing multiple variables as model inputs provided the models better accuracy. The evaluation of the CCPP modification indicates that the PHE brought a maximum increase of 7.82 percentage points in combined cycle efficiency. (4) Conclusions: Recurrent Neural Networks, represented by GRU, are capable of predicting GSCC efficiency. Meanwhile, utilizing multiple inputs is essential to GSCC overall performance prediction. The research also proved the PHE to be effective in boosting GSCC thermal efficiency.

The ever-increasing world population, change in lifestyle, and limited natural water and energy resources have made industrial seawater desalination plants the leading contenders for cost-efficient freshwater production. In this study, the integration of a combined cycle power plant (CCPP) with multi-effect distillation (MED) and reverse osmosis (RO) desalination units is investigated through comprehensive conventional and advanced exergy, exergoeconomic, and exergoenvironmental analyses. Firstly, the thermodynamic modelling of the CCPP is performed by using a mathematical programming procedure. Then, a mathematical model is developed for the integration of the existing CCPP plant with MED and RO desalination units. Finally, conventional and advanced exergy, exergoeconomic, and exergoenvironmental analyses are carried out to assess the main performance parameters of the integrated CCPP and MED-RO desalination system, as well as to identify potential technical, economic, and environmental improvements. A case study is presented based on the Shahid Salimi Neka power plant located at the north of Iran along the Caspian Sea. The mathematical modelling approach for the integrated CCPP and MED-RO desalination system is solved in MATLAB, and the results are validated via Thermoflex software. The results reveal an increase of 3.79% in fuel consumption after the integration of the CCPP with the desalination units. The exergy efficiency of the integrated system is 42.7%, and the highest cost of exergy destruction of the combustion chamber is 1.09 US $ per second. Economic and environmental analyses of the integrated system also show that gas turbines present the highest investment cost of 0.047 US$per second. At the same time, MED exhibits the highest environmental impact rate of 0.025 points per second.

Autonomous underwater vehicles (AUVs) are required to thoroughly scan designated areas during underwater missions. They typically follow a zig-zag trajectory to achieve full coverage. However, effective coverage can be challenging in complex environments due to the accumulation and drift of navigation errors. Possible solutions include surfacing for satellite positioning or underwater acoustic positioning using transponders on other vehicles. Nevertheless, surfacing or active acoustics can compromise stealth during reconnaissance missions in hostile areas by revealing the vehicle's location. In this paper, we propose calibration and planning strategies based on error models and acoustic positioning to address this challenge. We deploy acoustic markers via surface vessels to reduce navigation errors while maintaining stealth. We present a novel path-planning method for complete-area coverage by AUVs using an unscented Kalman filter and acoustic positioning. By analyzing the statistics of accumulated sensor errors, we optimize the positions of acoustic markers to communicate with AUVs and achieve better coverage. AUV trajectory concealment is achieved during detection by randomizing the USV navigation trajectory and irregularizing the locations of acoustic marker. Simulations based on large-scale maps demonstrate the effectiveness and robustness of the proposed algorithm.

A security constrained non-convex power dispatch problem with prohibited operation zones and ramp rates is formulated and solved using an iterative solution method based on the feasible modified sub-gradient algorithm (FMSG). Since the cost function, all equality and inequality constraints in the nonlinear optimization model are written in terms of the bus voltage magnitudes, phase angles, off-nominal tap settings, and the Susceptance values of static VAR (SVAR) systems, they can be taken as independent variables. The actual power system loss is included in the current approach and the load flow equations are inserted into the model as the equality constraints. The proposed modified sub gradient based combined objective technique and evolutionary programming approach (MSGBCAEP) with as decision variable and cost function as fitness function is tested on the IEEE 30-bus 6 generator test case system. The absence of crossover operation and adoption of fast judicious modifications in initialization of parent population, offspring generation and normal distribution curve selection in EP enables the proposed MSGBCAEP approach to ascertain global optimal solution for cost of generation and emission level shown in Table 6 and displayed in Figure 2 and Figure 3 respectively.

Due to the difficult problem of avoiding obstacles to achieve the complete coverage path planning (CCPP) for special missions, this paper introduces a novel integrated algorithm of CCPP for autonomous mobile robot under an obstacles-included environment. The algorithm combines cellular decomposition approach and the Standard map together for designing. The cellular decomposition approach is used to simplify the given workplace into smaller sub-regions for coverage via a chaotic path planner. The planner is constructed based on the chaotic Standard map at full mapping and produces the needed trajectories inside each decomposed sub-region. The simulation results verify the effectiveness of the designed method.

Water corrosiveness depends mainly on the chemical factors of pH, alkalinity, Ca2 +  concentration, dissolved oxygen, and total dissolved solids (TDS), and on the physical factors of temperature and flow velocity as well as pipe materials. The calcium carbonate precipitation potential (CCPP) control process and a simulated water distribution system (SWDS) were installed for a pilot-scale advanced water treatment process. The system was operated for 2 years. The CCPP control algorithm for anti-corrosion of a pipeline was developed and validated. The target CCPP value could be controlled by manipulating the pH and alkalinity with additions of sodium carbonate (Na2CO3) and carbon dioxide (CO2) where enough calcium was present. The CCPP range of 0 ∼ 4 mg L−1 was controlled reasonably to induce a calcium carbonate (CaCO3) film on the surface of the pipeline, which provided the anti-corrosion effect. The proper range required of pH and alkalinity used to manipulate the range of 0 ∼ 4 mg L−1 of CCPP was 8.0 ∼ 8.3, 70 ∼ 100 mg L−1 as CaCO3 when the Ca2 +  concentration was in the range of 60 ∼ 80 mg L−1 as CaCO3, respectively, in this research. The effect of corrosion control was demonstrated by reduced iron and zinc concentrations released from the pipe material. This result might indicate the presence of the CaCO3 film and the efficacy of its anti-corrosion effect. However, the simple proportional integral derivative (PID) controller's sensitivity seemed to be in need of further improvement.