Low-cost Solution Research Articles

Optimizing Hybrid Perovskites for High-Efficiency Solar Energy

Our research explores hybrid perovskite solar cells, promising low-cost and high-efficiency photovoltaic solutions. Despite challenges, optimizations like interface engineering enhance performance for future commercial use. Study of different physical parameters with the help of SILVACO and MATLAB Simulink for cell modeling. Long-term stability and cost reductions are among the most significant challenges to broad commercialization Future research will be around trying different parameter values. There searcher said this project represents an important step forward in the development of hybrid perovskite solar cells, which could be a key partof a new wave of clean energy sources that are needed as global consumption levels continue to rise. The solar cells studied herein are detailed by the six-scan structure: Glass/Indium Tin Oxide (ITO) / Electron Transport Layer - (N layer) - Active Layer (Perovskite) - Hole transport_layer (Payer) - Metal Electrode; Glass/ITO/TiO2/Pero_Aardes Spiro-OMeTAD/Au. It delivers the energy conversion efficiency of 19.12% with a fill factor (FF) of 80.53%, an open-circuit voltage at Voc =1.14 Vandshort circuit current density Jsc=20.88 mA/cm²,respectively The active layer is a 0.25 -thick CH3NH3PbI3 perovskite, and the TiO2 layer thickness was set at 0.02

Development and validation of the Florey Dementia Risk Score web-based tool to screen for Alzheimer's disease in primary care

It is estimated that ∼60% of people with Alzheimer's disease (AD) are undetected or undiagnosed, with higher rates of underdiagnosis in low-to middle-income areas with limited medical resources. To promote health equity, we have developed a web-based tool that utilizes easy-to-collect clinical data to enhance AD detection rate in primary care settings. This study was leveraged on the data collected from participants of the Australian Imaging, Biomarker & Lifestyle (AIBL) study and the Religious Orders Study and Memory and Aging Project (ROSMAP). The study included three phases: (1) constructing and evaluating a model on retrospective cohort data (1407 AIBL participants), (2) performing simulated trials to assess model accuracy (30 AIBL participants) and missing data tolerability (30 AIBL participants), and (3) external evaluation using a non-Australian dataset (500 ROSMAP participants). The auto-score machine learning algorithm was employed to develop the Florey Dementia Risk Score (FDRS). All the simulated trials and evaluation were performed using a web-based FDRS tool. FDRS achieved an area under the curve (AUC) of approximately 0.82 [95% CI, 0.75-0.88], with a sensitivity of 0.74 [0.60-0.86] and a specificity of 0.73 [0.70-0.79]. The accuracy of the simulated pilot trial for 30 AIBL participants with complete record was 87% (26/30 correct), while it only slightly decreased (80.0-83.3%, depending on imputation methods) for another 30 AIBL participants with one or two missing data. FDRS achieved an AUC of 0.82 [0.77-0.86] of 500 ROSMAP participants. The FDRS tool offers a potential low-cost solution to AD screening in primary care. The present study warrants future trials of FDRS for optimization and to confirm its generalizability across a more diverse population, especially people in low-income countries. National Health and Medical Research Council, Australia (GNT2007912) and Alzheimer's Association, USA (23AARF-1020292).

Improvement of low-cost solar-powered desalination technologies in low-light intensity

Enhancing the efficiency of low-cost solar-powered desalination technologies, such as solar stills (SS), is essential for ensuring continuous access to freshwater in remote, water-stressed areas, particularly during cloudy or rainy days when the performance of conventional SS systems is compromised. This study introduces an innovative stepped solar still design, optimized for ease of operation, maintenance, environmental compatibility, and improved efficiency, especially under low-light conditions. The impact of the inlet mass flow rate on the desalination process was investigated to enhance distilled water production. Light absorption and step hot spot temperatures were further improved by incorporating natural and cost-effective absorbers, such as carbon, and an innovative soil-carbon combination. The synergy of this soil-carbon combination, enhancing light absorption, heat transfer, and storage through increased surface contact during radiation exposure and its distribution during shutdown, led to a 12.8% and 3% increase in distilled water production over the 4-hour test duration, compared to the baseline and carbon-only tests, respectively. This innovative design, combined with the use of a soil and carbon mixture, significantly improves the performance of solar distillation systems. These findings contribute to the development of sustainable, low-cost, and energy-efficient solutions for freshwater provision in remote areas, addressing both water scarcity and energy conservation challenges.

High-Resolution and Robust One-Bit Direct-of-Arrival Estimation via Reweighted Atomic Norm Estimation.

In recent years, one-bit quantization has attracted widespread attention in the field of direction-of-arrival (DOA) estimation as a low-cost and low-power solution. Many researchers have proposed various estimation algorithms for one-bit DOA estimation, among which atomic norm minimization algorithms exhibit particularly attractive performance as gridless estimation algorithms. However, current one-bit DOA algorithms with atomic norm minimization typically rely on approximating the trace function, which is not the optimal approximation and introduces errors, along with resolution limitations. To date, there have been few studies on how to enhance resolution under the framework of one-bit DOA estimation. This paper aims to improve the resolution constraints of one-bit DOA estimation. The log-det heuristic is applied to approximate and solve the atomic norm minimization problem. In particular, a reweighted binary atomic norm minimization with noise assumption constraints is proposed to achieve high-resolution and robust one-bit DOA estimation. Finally, the alternating direction method of multipliers algorithm is employed to solve the established optimization problem. Simulations are conducted to demonstrate that the proposed algorithm can effectively enhance the resolution.

A precise prediction of structure stability and hydrogen storage capability of KCdH3 perovskite hydride using density functional theory calculations

Perovskite hydrides materials are acknowledged for hydrogen storage due to their high capacity, reversibility, thermodynamic stability, tunable nature, and potential low-cost solutions. In the current investigation, the four distinct cubic structures of perovskite hydride KCdH3 for the hydrogen storage are investigated with the help of density functional theory and providing valuable insights into stable phases. Electronic, thermal and entropy calculations are performed. Two out of four cubic structure are founded mechanically stable with the gravimetric storage capacity of be 5.5 cwt% and the volumetric storage capacity of 67.69 gH2l−1 which align with the standard situated by the U.S. Department of Energy for the year 2025. Notably, this study is the first comprehensive investigation of all four cubic phases of the KCdH3 compound. The promising hydrogen storage capabilities of these compounds shows the credibility for their effective use as a solid-state hydrogen storage material, and the research aims to further ascertain its stability and storage capacities.

Maximizing the IRS’s Gains in NLOS Communication through Optimal Positioning

Background: Recently, an IRS has been proposed as a low-cost and effective solution for performance improvement in wireless communication. However, the configuration and setup necessary to derive optimal results have not been identified yet. Objective: The paper seeks to build the knowledge by investigating the effects of IRS in the LOS and NLOS environment to verify the achievable gains from each and examine how different IRS positions impact the BER. Methods: An IRS model was simulated in MATLAB to investigate the channel capacity gain of an IRS in LOS and NLOS scenarios for the verification of performance gains. Further, the effects of the IRS positioning (with respect to transmitter and end-user) on the BER were investigated. Results: The channel capacity of an IRS system was found to be lower by 12Mbps compared to that of a direct link in a LOS scenario. This demonstrates that a passive IRS is only beneficial in the absence of a line of sight. In the case of a NLOS, for an SNR of 50, MIMO transmitter diversity and receiver diversity had channel capacities of 1.0 Mbps and 4.5 Mbps, respectively while the IRS system achieved 22 Mbps. As such, it is more beneficial to create an alternative path via the IRS than to increase the number of transmit or receive antennas. The positioning of the IRS had a significant impact on the BER. From the results, positioning the IRS mid-way between the AP and STA led to up to 57.69% improvements in BER compared to close to STA or close to AP arrangements. Conclusion: The results prove that an IRS is a cost-effective, low-power consumption solution for overcoming challenges associated with NLOS communication. The work also identified the ideal positioning of the IRS to be the midpoint between AP and STA, for the WLAN system. Further research will be needed to verify performance in other systems.

Evaluation of Sugarcane Crop Growth Monitoring Using Vegetation Indices Derived from RGB-Based UAV Images and Machine Learning Models

Crop monitoring with unmanned aerial vehicles (UAVs) has the potential to reduce field monitoring costs while increasing monitoring frequency and improving efficiency. However, the utilization of RGB-based UAV imagery for crop-specific monitoring, especially for sugarcane, remains limited. This work proposes a UAV platform with an RGB camera as a low-cost solution to monitor sugarcane fields, complementing the commonly used multi-spectral methods. This new approach optimizes the RGB vegetation indices for accurate prediction of sugarcane growth, providing many improvements in scalable crop-management methods. The images were captured by a DJI Mavic Pro drone. Four RGB vegetation indices (VIs) (GLI, VARI, GRVI, and MGRVI) and the crop surface model plant height (CSM_PH) were derived from the images. The fractional vegetation cover (FVC) values were compared by image classification. Sugarcane plant height predictions were generated using two machine learning (ML) algorithms—multiple linear regression (MLR) and random forest (RF)—which were compared across five predictor combinations (CSM_PH and four VIs). At the early stage, all VIs showed significantly lower values than later stages (p < 0.05), indicating an initial slow progression of crop growth. MGRVI achieved a classification accuracy of over 94% across all growth phases, outperforming traditional indices. Based on the feature rankings, VARI was the least sensitive parameter, showing the lowest correlation (r < 0.5) and mutual information (MI < 0.4). The results showed that the RF and MLR models provided better predictions for plant height. The best estimation results were observed withthe combination of CSM_PH and GLI utilizing RF model (R2 = 0.90, RMSE = 0.37 m, MAE = 0.27 m, and AIC = 21.93). This study revealed that VIs and the CSM_PH derived from RGB images captured by UAVs could be useful in monitoring sugarcane growth to boost crop productivity.

Estimation of corn crop damage caused by wildlife in UAV images

PurposeThis paper proposes a low-cost and low-effort solution for determining the area of corn crops damaged by the wildlife facility utilising field images collected by an unmanned aerial vehicle (UAV). The proposed solution allows for the determination of the percentage of the damaged crops and their location.MethodsThe method utilises image segmentation models based on deep convolutional neural networks (e.g., UNet family) and transformers (SegFormer) trained on over 300 hectares of diverse corn fields in western Poland. A range of neural network architectures was tested to select the most accurate final solution.ResultsThe tests show that despite using only easily accessible RGB data available from inexpensive, consumer-grade UAVs, the method achieves sufficient accuracy to be applied in practical solutions for agriculture-related tasks, as the IoU (Intersection over Union) metric for segmentation of healthy and damaged crop reaches 0.88.ConclusionThe proposed method allows for easy calculation of the total percentage and visualisation of the corn crop damages. The processing code and trained model are shared publicly.

Cost–benefit analysis on the implementation of nature-based treated wastewater reuse: case of sekem farm El-Wahat, Egypt

In a context of increasing pressure on water resources and severe aridity; expansion in the reuse of naturally treated wastewater can be a viable and low-cost solution particularly for irrigation forests, green non-fruit trees, fabric crops, industrial oils, and non-edible raw crops. Wastewater treatment is increasingly recognized as a potential means in El-Wahat El-Bahariya in Egypt. However, investment decisions concerning the reuse of treated wastewater in irrigation needs to be justified in terms of financial and economic feasibility and profitability. Therefore, this research study aims to conduct a cost–benefit analysis (CBA) of an investment project “reuse treated wastewater in irrigation compared to other modes of water irrigation projects. The CBA results revealed that the cost of the initial investment for the production of treated water used for irrigating green non-fruit trees, Bamboo trees, and Cactus in Sekem El-Wahat, is economically efficient with 88% compared to the exploitation of aquifer groundwater for irrigating the same crops. That project allows for an economic gain of about 4,428.5 €/ha compared to the cost of producing aquifer ground water in Sekem Farm El-Wahat is estimated at 10,800 €/year. Substituting the use of aquifer ground water for irrigating crops with reuse of treated wastewater helps reducing energy consumption and offers great financial benefits to the beneficial communities. A net benefit of the reuse of treated wastewater project is largely positive with NPV equivalent to 4599 €/year with a medium economic efficiency (BCR) of about 0.44.

Building aluminium-air battery on waste paper for DIY learning

Abstract Environmental sustainability and the effective use of renewable resources are critical subjects that need to be integrated into our educational systems through practical, hands-on learning. This paper focuses on the fabrication of aluminium-air batteries using waste materials, providing an innovative and cost-effective DIY approach to energy storage education. Utilizing aluminium from food packaging and graphite from pencils, this study demonstrates the construction and functionality of an aluminium-air battery to run an LED, highlighting its potential applications in educational settings and low-cost energy solutions. The paper aims to inspire the adoption of environmentally friendly practices and enhance students’ understanding of electrochemical energy conversion through accessible and engaging DIY projects.

CLARA: A cluster-based node correlation for sampling rate adaptation and fault tolerance in sensor networks

Recently, wireless sensor networks (WSNs) have been proven as an efficient and low-cost solution for monitoring various kind of applications. However, the massive amount of data collected and transmitted by the sensor nodes, which are mostly redundant, will quickly consume their limited battery power, which is sometimes difficult to replace or recharge. Although the huge efforts made by researchers to solve such problem, most of the proposed techniques suffer from their accuracy and their complexity, which is not suitable for limited-resources sensors. Therefore, designing new data reduction techniques to reduce the raw data collected in such networks is becoming essential to increase their lifetime. In this paper, we propose a CLuster-based node correlation for sAmpling Rate adaptation and fAult tolerance, abbreviated CLARA, mechanism dedicated to periodic sensor network applications. Mainly, CLARA works on two stages: node correlation and fault tolerance. The first stage introduces a data clustering method that aims to search the correlation among neighboring nodes. Then, it accordingly adapts their sensing frequencies in a way to reduce the amount of data collected in such networks while preserving the information integrity at the sink. In the second stage, a fault tolerance model is proposed that allows the sink to regenerate the raw sensor data based on two methods: moving average (MA) and exponential smoothing (ES). We demonstrated the efficiency of our technique through both simulations and experiments. The best obtained results show that the first stage can reduce the sensor sampling rate, and accordingly the sensor energy, up to 64% while the second stage can accurately regenerate the raw data with an error loss less than 0.15.

A System Architecture for Early Wilt Detection in Hydroponic Crops: An Implementation and Assessment

Abstract Hydroponic farming systems have a high risk of failure if the disaster is not mitigated and responded to quickly. Hydroponic plants only rely on mineral water that flows through their roots as a source of life; thus, any malfunction will have an immediate impact on the plant. If plant wilting is not recognized and not treated quickly, the plant will experience stress and have the potential to fail. To ensure that major disasters do not occur, an early warning mechanism is needed that can provide disaster information to farmers. In this study, an intelligent system architecture was built to detect premature wilt in hydroponic vegetable plants. It combines the capabilities offered by the OV2640 image sensor and ESP32-S system-on-chip, the Internet of Things, deep learning based on convolutional neural networks, and cloud computing to create a robust and low-cost remote plant condition monitoring solution. As a proof-of-concept, a prototype system is built, and performance tests are carried out to find out how robust and effective the proposed design is. Based on the results of the prototype test, it can be seen that the system built can run properly with an accuracy of wilting recognition of up to 90.90%, which was tested on samples of vertically grown mustard plants in a hydroponic greenhouse.

A deep learning model for estimating body weight of live pacific white shrimp in a clay pond shrimp aquaculture

This paper presents a novel approach to address the essential challenge of accurately determining the total weight of shrimp within aquaculture ponds. Precise weight estimation is crucial in mitigating issues of overfeeding and underfeeding, thus enhancing efficiency and productivity in shrimp farming. The proposed system leverages image processing techniques to detect individual live shrimp and extract pertinent features for weight estimation within a clay pond environment. Specifically, an automated feed tray captures images of live shrimp, which are then processed using a combination of Detectron2, PyTorch, and CUDA (Compute Unified Device Architecture) for individual shrimp detection. Essential features such as area, perimeter, width, length, and posture are extracted through image analysis, enabling accurate estimation of shrimp weight. An Artificial Neural Network (ANN) model, utilizing these features, accurately predicts shrimp weight with a coefficient of determination (R2) of 94.50% when incorporating all extracted features. Furthermore, our system integrates a user-friendly web application that empowers farmers to monitor shrimp weight trends, facilitating precision feeding strategies and effective farm management. This study contributes a low-cost solution using a deep learning model to estimate the weight of live Pacific white shrimp in clay ponds, enabling daily weight calculations, helping farmers optimize feed quantities, providing shrimp size distribution insights, and reducing the Feed Conversion Ratio (FCR) for greater profitability. The procedure for shrimp feature extraction is also provided, including the calculation of shrimp length and width, as well as shrimp posture classification.

Ultrasonic spray synthesis, photoelectric properties and photovoltaic performances of chalcogenide CaSnS3

Chalcogenide perovskites are promising lead-free, stable absorber materials for solar cells. This work reports the synthesis of orthorhombic phase pure CaSnS3 thin films by facile low temperature sulfurization of solution-processed CaSnO3 oxide precursors. Structural characterization confirms complete anion exchange to produce crystalline CaSnS3 films with vertically aligned rod-like grains. Optical studies show strong visible light absorption with direct bandgap of 1.72 eV, ideal for photovoltaics. Electrical measurements indicate p-type conductivity with hole concentration of 1.2×1017 cm-3 and mobility around 8 cm2V-1s-1 at room temperature. First-principles DFT calculations corroborate the p-type electronic structure. Prototype CaSnS3 solar cells are fabricated with TiO2 electrode, demonstrating power conversion efficiency of 2.5% under AM1.5G, open-circuit voltage of 0.55 V, short circuit current density of 11.5 mA/cm2 and fill factor of 0.62. The cells also exhibit remarkable ambient shelf stability over 6 months. The comprehensive results validate the photovoltaic potential of these earth abundant, sustainable chalcogenide perovskites synthesized via scalable low-cost solution methods. Further interface engineering can enable enhanced efficiencies.

Enhancing Data Integrity with Efficient Retention-Refilling Programming Schemes

The retention error has become one of the most challenging reliability issues of flash memory due to the shrinking of the technology nodes. To enhance data integrity by resolving the retention error issues for 3D MLC flash memory devices (e.g., SSDs and SD cards), many excellent works that exploited in-place reprogramming and data refreshing concepts have been proposed in recent years. However, these approaches could result in additional issues, such as programming disturbance and performance overhead (e.g., unavoidable data refreshing and a larger amount of program and verify shots). This work is motivated by the need to explore a low-cost solution to resolve retention error issues without incurring negative impacts caused by conventional refresh-based and in-place reprogramming approaches. As a result, this work exploits the characteristics of the cell's V t distribution and proposes the novel concept of "retention-refilling" to enhance data integrity. With such an idea, a retention-refillable programming scheme is proposed to improve flash reliability and mitigate performance overheads by trading data refreshing with retention-refilling. The capability of the proposed scheme is evaluated by a series of experiments, for which we have very encouraging results.

Early post-operative outcome of pre-pectoral implant-based immediate total breast reconstruction with Polyglactin 910 (Vicryl™) mesh – low cost solution for a low-middle income country

BackgroundThe incidence of breast cancer in Pakistan has been rising with approximately one third of these patients requiring mastectomy. Among breast reconstruction treatment options, the use of Acellular Dermal Matrix (ADM) for pre-pectoral breast implant surgery has proven effective with improved cosmetic outcome. However, due to high cost it cannot be regularly implemented in a developing country like Pakistan. An alternative to ADM, Polyglactin 910 (Vicryl™, Ethicon) mesh has been introduced in pre-pectoral breast reconstructive surgery which has shown to be almost 10 times lower in cost. We set out to determine the frequency of early postoperative complications when using Polyglactin 910 mesh for pre-pectoral implant-based breast reconstruction surgery.MethodsA single centre, retrospective, chart review was conducted, and a total 28 women were included in the study. Thirty-two pre-pectoral implant-based mastectomies with Polyglactin 910 mesh were performed. Early post-operative outcomes (within 12 months of procedure) including duration of antibiotic use, post-operative infection, implant displacement, flap necrosis, seroma formation, wound dehiscence, hematoma formation, capsular contracture and reconstruction failure, were recorded.ResultsOnly 4 (12.5%) women experienced early post-operative morbidity. One patient developed a wound dehiscence, which eventually led to reconstruction failure and removal of the implant. Another patient had seroma formation and flap necrosis. None of the patients developed postoperative implant displacement, hematoma formation or capsular contracture in the early post-operative period.ConclusionThis study reveals that early post-operative outcomes with Polyglactin 910 mesh in breast reconstructive surgery are few, thus making it a cost effective, reliable, and safe treatment option, especially in developing countries like Pakistan.

Standalone edge AI-based solution for Tomato diseases detection

Tomato yield is significantly affected by diseases, which are a continuous challenge for its production and pose threats to its global supply chain. Automatic and early detection of these diseases could help growers to swiftly adopt mitigation strategies to limit the disease spread, leading to improved production. Deep learning-based CNN approaches have been widely applied to detect tomato diseases. However, deep learning models are highly computationally demanding, resulting in a computational bottleneck for practical adaptation for agricultural applications such as disease detection and monitoring. Over the last few years, developments of open-source Edge systems have provided opportunities for low-cost and low-power consumption practical solutions for deep learning applications for agriculture. Therefore, the primary goal of this study was to evaluate the performance of standalone Edge-AI solutions for tomato leaf disease detection. To achieve this goal, firstly, this study employed lightweight deep learning networks to detect and differentiate tomato leaf diseases (bacterial spot, early blight, healthy, late blight, leaf mold, septoria leaf spot, two spotted spider mites, target spot, and yellow leaf curl virus). Then, these deep learning networks were deployed on low-cost and low-power consumption Edge devices to investigate their performance capabilities as standalone Edge-AI solutions for the early detection of tomato leaf diseases. Lightweight CNN based GoogleNet and MobileNetV2 deep learning networks achieved accuracies of up to 98.25 % compared to accuracies of 98.13 %, 98.13 %, 94.62 %, and 90.67 % of EfficientNetB0, ResNet-18, SqueezeNet, and NasNetMobile, respectively, in detecting tomato diseases. NVIDIA Jetson ORIN AGX and Nano significantly outperformed Raspberry Pi and Raspberry Pi with AI accelerator (Google Coral) in image classification, achieving classification times of 3.5 ms and 5.2 ms respectively, using SqueezeNet, compared to 15.3 ms and 10.5 ms on the Raspberry Pi devices. In addition, Raspberry Pi with Google Coral achieved the best cost/FPS performance of 0.14 compared to other Edge devices NVIDIA Jetson AGX Orin and NVIDIA Jetson Nano Orin with cost/FPS of 0.7 and 0.26, respectively. These results showed the potential of standalone Edge-AI solutions using low-cost and low-power consuming software and hardware resources for early tomato disease detections.

Low-Cost Non-Wearable Fall Detection System Implemented on a Single Board Computer for People in Need of Care.

This work aims at proposing an affordable, non-wearable system to detect falls of people in need of care. The proposal uses artificial vision based on deep learning techniques implemented on a Raspberry Pi4 4GB RAM with a High-Definition IR-CUT camera. The CNN architecture classifies detected people into five classes: fallen, crouching, sitting, standing, and lying down. When a fall is detected, the system sends an alert notification to mobile devices through the Telegram instant messaging platform. The system was evaluated considering real daily indoor activities under different conditions: outfit, lightning, and distance from camera. Results show a good trade-off between performance and cost of the system. Obtained performance metrics are: precision of 96.4%, specificity of 96.6%, accuracy of 94.8%, and sensitivity of 93.1%. Regarding privacy concerns, even though this system uses a camera, the video is not recorded or monitored by anyone, and pictures are only sent in case of fall detection. This work can contribute to reducing the fatal consequences of falls in people in need of care by providing them with prompt attention. Such a low-cost solution would be desirable, particularly in developing countries with limited or no medical alert systems and few resources.

A systematic literature review of low-cost 3D mapping solutions

In "low-cost" solutions, ensuring economic accessibility and democratizing the availability of emerging technologies stand as pivotal considerations. This study undertakes a systematic literature review of low-cost 3D mapping solutions. Leveraging SCOPUS as the primary database, a comprehensive bibliometric analysis encompassing 1380 publications was conducted, subsequently narrowing the focus to 87 recent publications for detailed review. This research endeavors to delineate the defining characteristics of low-cost systems, elucidate their principal applications and preferred platforms, assess accessibility level, gauge the extent of innovation in both hardware and software development, explore the contributions of Deep Learning and data fusion, evaluate the consideration of data quality, and examine the contemporary relevance of photogrammetry within low-cost context. The findings demonstrate that many authors subjectively use the term low-cost to highlight qualities of a technology, methodology or sensor, but challenges arise from data quality comparisons with high-cost systems.

Coal allocation optimization based on a hybrid residual prediction model with an improved genetic algorithm

The objective of the coal blending optimization problem is to find an optimal coal blending in the feasible domain such that the blended coal meets the quality requirements at the end of the coking process and the cost of coal blending is minimized. This paper proposes a hybrid residual prediction model and an improved genetic algorithm to solve this problem and predict coke quality. For this purpose, a hybrid residual prediction model is used to predict coke quality. The model first uses a random forest feature extraction method to reduce the dimensionality of the data, and then trains several prediction models such as eXtreme Gradient Boosting (XGBoost), Adaboost and Light Gradient-Boosting Machine (lightGBM) for different coke indicators an improved genetic algorithm based on the adaptive weighted genetic algorithm (awGA) and another improved genetic algorithm based on a priori knowledge and adaptive random initialization method were designed and implemented to solve the optimization problem under strict constraints (P-awGA). The experimental results show that using the hybrid residual prediction model and the improved genetic algorithm can accurately predict the coke quality and use less time to obtain a lower-cost coal blending solution.