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An inverse problem concerning a diffusion equation with source control parameter is considered. The approximation of the problem is based on the Ritz method with satisfier function. The Ritz method together with the least squares approximation (Ritz-least squares method) are utilized to reduce the inverse problem to the solution of algebraic equations. We extensively discuss the convergence of the method and finally present illustrative examples to demonstrate validity and applicability of the new technique.

Permanent magnet synchronous motors (PMSM) have become prevalent in industry and play an essential role in managing industrial processes, automation systems, and renewable energy sources due to their superior efficiency, torque, and power density. However, because it operates like a non‐linear system with quick dynamics, variable parameters during operation, and unknown disturbances, PMSM presents challenges for machine control. Non‐linear controls are required to account for the non‐linearities of the permanent magnet synchronous machine. Recently, predictive control techniques for non‐linear multi‐variable systems have gained popularity. In this work, a novel approach to robust non‐linear generalized predictive control (RNGPC) has been developed for PMSM, with the aim of tracking the reference speed while maintaining minimum reactive power, robustness to external disturbances, and parameter uncertainties. A new finite horizon cost function is integrated, with an integral action introduced in the control law. The main advantage of this technique is that it does not require the measurement and observation of external disturbance as well as parametric uncertainties. The control strategy method has been tested in the MATLAB/Simulink environment with various operating conditions. The results showed good robustness against parameter changes and ensured fast convergence. In this work, a new approach of robust non‐linear generalized predictive controller (RNGPC) has been developed for permanent magnet synchronous motors (PMSM). The control objective is tracking the reference speed while maintaining minimum reactive power and robustness to external disturbances and parameter uncertainties.

Multiple free water surface flow constructed wetlands (multi-FWS CWs) are a variety of conventional water treatment plants for the interception of pollutants. This review encapsulated the characteristics and applications in the field of ecological non-point source water pollution control technology. The roles of in-series design and operation parameters (hydraulic residence time, hydraulic load rate, water depth and aspect ratio, composition of influent, and plant species) for performance intensification were also analyzed, which were crucial to achieve sustainable and effective contaminants removal, especially the retention of nutrient. The mechanism study of design and operation parameters for the removal of nitrogen and phosphorus was also highlighted. Conducive perspectives for further research on optimizing its design/operation parameters and advanced technologies of ecological restoration were illustrated to possibly interpret the functions of multi-FWS CWs.

Manufacturing sector is considered to be the second highest contributor in greenhouse gases emissions in EU, secondary to energy sector. The environmental impact of products, processes, and infrastructures of manufacturing is defined as the mass equivalent of carbon dioxide emissions, also known as carbon footprint, because carbon dioxide accounts for the largest portion of greenhouse gases emissions. The aim of this review is to show the impact of manufacturing on carbon emissions and to investigate the importance of carbon emission factors on the carbon footprint of manufacturing. This was performed via (1) mapping and categorizing the sources of carbon emission at process, machine, and system level; (2) identifying the weight factor of carbon emissions factors via sensitivity analysis; and (3) determining which carbon emission factor has the heaviest contribution in carbon footprint calculation. In all examples of the sensitivity analysis, it was shown that carbon emission factor for electrical energy was the only contributing factor at process level while being the strongest at machine level. At system level, the strongest contributor was the carbon emission factor for material production. To reduce the carbon emissions, one must identify the tuneable parameters at process, machine, and system level, from material, machine tool, and energy point of view. However, the highest reduction in carbon footprint can be achieved by reducing the carbon emission factors of electrical energy using renewable power sources such as solar or wind and by reducing the carbon emission factors for material production using recycling materials as “raw” material.

As a result of great increases in CO 2 emissions in the last few decades, many papers have examined the relationship between renewable energy and CO 2 emissions in the energy economics literature, because as a clean energy source, renewable energy can reduce CO 2 emissions and solve environmental problems stemming from increases in CO 2 emissions. When one analyses these papers, he/she will observe that they employ fixed parameter estimation methods, and time-varying effects of non-renewable and renewable energy consumption/production on greenhouse gas emissions are ignored. In order to fulfil this gap in the literature, this paper examines the effects of non-renewable and renewable energy on CO 2 emissions in Turkey over the period 1970–2013 by employing fixed parameter and time-varying parameter estimation methods. Estimation methods reveal that CO 2 emissions are positively related to non-renewable energy and renewable energy in Turkey. Since policy makers expect renewable energy to decrease CO 2 emissions, this paper argues that renewable energy is not able to satisfy the expectations of policy makers though fewer CO 2 emissions arise through production of electricity using renewable sources. In conclusion, the paper argues that policy makers should implement long-term energy policies in Turkey.

Constructed wetlands (CWs) can be considered as an efficient nature-based solution for the treatment of agricultural drainage water (ADW) and consequently for the mitigation of non-point source pollution. Aiming to provide suggestions for the construction and implementation of CWs, this paper proposes and discusses key parameters of CW design and operation. In order to verify the effect of these features, different case studies were reviewed, focusing on the performance of CWs that are treating agricultural drainage water. The findings showed that design and operational factors (e.g., the application of simple hydraulic structures and vegetation establishment) can improve pollutant removal efficiencies by increasing hydraulic retention time. Hydraulic efficiency of CWs can also be enhanced through certain shape characteristics (e.g., adoption of a high aspect ratio and creation of a long and narrow CW shape). The careful consideration of these parameters before and during CW implementation can therefore help these systems to achieve their full potential. However, further study is recommended to assess the effects of some parameters (e.g., flow direction and the application of deep zones).

To properly simulate the laser directed energy deposition (L-DED) process, the heat source model is crucial as it represents the absorbed heat energy, which controls the thermal phenomena associated with L-DED. Goldak’s double-ellipsoidal heat source model has been broadly used to represent energy distribution in a wide range of laser-based simulation processes. It is therefore important to accurately determine Goldak’s model parameters for correct L-DED simulation. In this work, an original inverse identification procedure was proposed to determine Goldak’s heat source parameters by coupling a 3D finite element thermal model with a genetic algorithm. The multi-objective Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used by considering two objective functions simultaneously. The temperatures at specific points in the melt pool were extracted from the 3D finite element simulations and then used to construct objective functions for the optimization procedure. By minimizing the differences between the simulated and reference temperatures, Goldak’s heat source parameters were identified based on experimental L-DED fabricated single-track deposits for different process conditions. The model accurately estimated the penetration depth and slightly overestimated the heat-affected zone (HAZ). The relative errors are reliable enough to reasonably predict Goldak’s heat source model parameters. The inverse identification procedure proposed in this study can help reduce time and experimental costs to estimate heat source parameters for the simulation of the L-DED process.

The concept of Islanded Hybrid Power System (IHPS) has attracted considerable interest lately, especially for energizing remote or energy-poor locations. IHPS are more dependable and cost-effective alternatives to systems using only one energy source when properly constructed. IHPS configuration, including Diesel Engine Generator (DEG), Photovoltaic (PV) systems, and Battery Storage (BATT) elements, are desirable for islanded systems about price and dependability. IHPS mostly use Renewable Energy Sources (RES) for power production, which is variable. Consequently, these variations often make it difficult for traditional control systems to maximize efficiency across various operating environments. The current research discusses the requirement for more effective frequency control in IHPS by suggesting a Model Reference Adaptive Control-Fuzzy Proportional Integral based Whale Optimization Algorithm (MRAC-FPI-WOA) controller. The proposed controller can efficiently manage a range of disturbances by dynamically adjusting its control techniques. The current research conducts an evaluation study comparing the effectiveness of the suggested MRAC-FPI-WOA controller against FPI-WOA, PI-WOA, and PI-PSO controllers. The key evaluation criteria are the ability to maintain stability in frequency within the IHPS and the effectiveness of power production in the overall system. The results demonstrate the superior performance of the MRAC-FPI-WOA controller across diverse operational scenarios. Notably, during a three-phase fault at Bus2, the MRAC-FPI-WOA controller achieves significant performance enhancements over the PI-PSO controller, with reductions of 59.05% in maximum overshoot (% ), 72.83% in maximum undershoot (% ), 32.07% in settling time ( ), and 34.81% in the integral of time-weighted absolute error (ITAE). A similar trend is observed during a three-phase fault at the tie-line, where the MRAC-FPI-WOA controller yields improvements of 57.47% in % , 79.36% in % , 40.9% in , and 78.08% in ITAE. Furthermore, the controller exhibits exceptional dynamic responsiveness to ramp variations in solar radiation, substantially reducing % by 96.72%, % by 95.24%, by 22.79%, and ITAE by 89.69%. Additionally, it demonstrates robust adaptability to random solar radiation fluctuations, consistently optimizing transient response with reductions of 96.63% in % , 99.58% in % , 22.07% in , and 95.23% in ITAE.

Due to the increased complexity and nonlinear nature of microgrid systems such as photovoltaic, wind-turbine fuel cell, and energy storage systems (PV/WT/FC/ESSs), load-frequency control has been a challenge. This paper employs a self-tuning controller based on the fuzzy logic to overcome parameter uncertainties of classic controllers, such as operation conditions, the change in the operating point of the microgrid, and the uncertainty of microgrid modeling. Furthermore, a combined fuzzy logic and fractional-order controller is used for load-frequency control of the off-grid microgrid with the influence of renewable resources because the latter controller benefits robust performance and enjoys a flexible structure. To reach a better operation for the proposed controller, a novel meta-heuristic whale algorithm has been used to optimally determine the input and output scale coefficients of the fuzzy controller and fractional orders of the fractional-order controller. The suggested approach is applied to a microgrid with a diesel generator, wind turbine, photovoltaic systems, and energy storage devices. The comparison made between the results of the proposed controller and those of the classic PID controller proves the superiority of the optimized fractional-order self-tuning fuzzy controller in terms of operation characteristics, response speed, and the reduction in frequency deviations against load variations. Graphical abstract

Climate predictions tailored to the wind energy sector represent an innovation in the use of climate information to better manage the future variability of wind energy resources. Wind energy users have traditionally employed a simple approach that is based on an estimate of retrospective climatological information. Instead, climate predictions can better support the balance between energy demand and supply, as well as decisions relative to the scheduling of maintenance work. One limitation for the use of the climate predictions is the bias, which has until now prevented their incorporation in wind energy models because they require variables with statistical properties that are similar to those observed. To overcome this problem, two techniques of probabilistic climate forecast bias adjustment are considered here: a simple bias correction and a calibration method. Both approaches assume that the seasonal distributions are Gaussian. These methods are linear and robust and neither requires parameter estimation—essential features for the small sample sizes of current climate forecast systems. This paper is the first to explore the impact of the necessary bias adjustment on the forecast quality of an operational seasonal forecast system, using the European Centre for Medium-Range Weather Forecasts seasonal predictions of near-surface wind speed to produce useful information for wind energy users. The results reveal to what extent the bias adjustment techniques, in particular the calibration method, are indispensable to produce statistically consistent and reliable predictions. The forecast-quality assessment shows that calibration is a fundamental requirement for high-quality climate service.