Summary Sun is an inexhaustible source of energy capable of fulfilling all the energy needs of humankind. The energy from the sun can be converted into electricity or used directly. Electricity can be generated from solar energy either directly using photovoltaic (PV) cells or indirectly using concentrated solar power (CSP) technology. Progress has been made to raise the efficiency of the PV solar cells that can now reach up to approximately 34.1% in multi‐junction PV cells. Electricity generation from concentrated solar technologies has a promising future as well, especially the CSP, because of its high capacity, efficiency, and energy storage capability. Solar energy also has direct application in agriculture primarily for water treatment and irrigation. Solar energy is being used to power the vehicles and for domestic purposes such as space heating and cooking. The most exciting possibility for solar energy is satellite power station that will be transmitting electrical energy from the solar panels in space to Earth via microwave beams. Solar energy has a bright future because of the technological advancement in this field and its environment‐friendly nature. The biggest challenge however facing the solar energy future is its unavailability all‐round the year, coupled with its high capital cost and scarcity of the materials for PV cells. These challenges can be met by developing an efficient energy storage system and developing cheap, efficient, and abundant PV solar cells. This article discusses the solar energy system as a whole and provides a comprehensive review on the direct and the indirect ways to produce electricity from solar energy and the direct uses of solar energy. The state‐of‐the‐art procedures being employed for PV characterization and performance rating have been summarized. Moreover, the technical, economic, environmental, and storage‐related challenges are discussed with possible solutions. Furthermore, a comprehensive list of future potential research directions in the field of direct and indirect electricity generation from solar energy is proposed.

Froth flotation process is extensively used for selective separation of base metal sulfides from uneconomic mineral resources. Reliable prediction of process outcomes (metal recovery and grade) is vital to ensure peak performance. This work employs an innovative hybrid machine learning (ML) model-constructed by combining the random forest model and the firefly algorithm-to predict froth flotation efficiency of galena and chalcopyrite in relation to various experimental process parameters. The hybrid model's prediction performance was rigorously evaluated, and compared against four different standalone ML models. The outcomes of this study illustrate that the hybrid ML model has the prediction ability to process outcomes with high-fidelity, while consistently outperforming the standalone ML models. K E Y W O R D Scomplex sulfide ore, firefly algorithm, froth flotation, machine learning, random forests INTRODUCTIONFroth flotation process is the most widely used practice to concentrate sulfide minerals-the main economic sources of world supplies of base metals. The flotation efficiency of metal sulfides is influenced by several key process variables (eg, water chemistry, reagents chemistry, feed characteristics, cell type, and aeration rate). [1][2][3] Although each of these parameters influences the flotation process outcomes (metal recovery and grade) independently, their interdependence

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The froth flotation process is extensively used for the selective separation of valuable base metal sulfides from uneconomic associated minerals. However, in this complex multiphase process, various parameters need to be optimized to ensure separation selectivity and peak performance. In this study, two machine learning (ML) models, artificial neural network (ANN) and random forests (RF), were used to predict the efficiency of in-house synthesized chitosan-polyacrylamide copolymers (C-PAMs) in the depression of iron sulfide minerals (i.e., pyrite) while valuable base metal sulfides (i.e., galena and chalcopyrite) were floated using nine flotation variables as inputs to the models. The prediction performance of the models was rigorously evaluated based on the coefficient of determination (R2) and the root-mean-square error (RMSE). The results showed that the RF model was able to produce high-fidelity predictions of the depression of pyrite once thoroughly trained as compared to ANN. With the RF model, the overall R2 and RMSE values were 0.88 and 4.38 for the training phase, respectively, and R2 of 0.90 and RMSE of 3.78 for the testing phase. As for the ANN, during the training phase, the overall R2 and RMSE were 0.76 and 4.75, respectively, and during the testing phase, the R2 and RMSE were 0.65 and 5.42, respectively. Additionally, fundamental investigations on the surface chemistry of C-PAMs at the mineral–water interface were conducted to give fundamental insights into the behavior of different metal sulfides during the flotation process. C-PAM was found to strongly adsorb on pyrite as compared to galena and chalcopyrite through zeta potential, X-ray photoelectron spectroscopy (XPS), and adsorption density measurements. XPS tests suggested that the adsorption mechanism of C-PAM on pyrite was through chemisorption of the amine and amide groups of the polymer.