Henry Gas Solubility Optimization Research Articles

Henry MaxNet: tversky index based feature selection and competitive swarm henry gas solubility optimization integrated Deep Maxout network for intrusion detection in IoT

Henry MaxNet: tversky index based feature selection and competitive swarm henry gas solubility optimization integrated Deep Maxout network for intrusion detection in IoT

An improved binary sparrow search algorithm for feature selection in data classification

Feature Selection (FS) is an important preprocessing step that is involved in machine learning and data mining tasks for preparing data (especially high-dimensional data) by eliminating irrelevant and redundant features, thus reducing the potential curse of dimensionality of a given large dataset. Consequently, FS is arguably a combinatorial NP-hard problem in which the computational time increases exponentially with an increase in problem complexity. To tackle such a problem type, meta-heuristic techniques have been opted by an increasing number of scholars. Herein, a novel meta-heuristic algorithm, called Sparrow Search Algorithm (SSA), is presented. The SSA still performs poorly on exploratory behavior and exploration-exploitation trade-off because it does not duly stimulate the search within feasible regions, and the exploitation process suffers noticeable stagnation. Therefore, we improve SSA by adopting: i) a strategy for Random Re-positioning of Roaming Agents (3RA); and ii) a novel Local Search Algorithm (LSA), which are algorithmically incorporated into the original SSA structure. To the FS problem, SSA is improved and cloned as a binary variant, namely, the improved Binary SSA (iBSSA), which would strive to select the optimal or near-optimal features from a given dataset while keeping the classification accuracy maximized. For binary conversion, the iBSSA was primarily validated against nine common S-shaped and V-shaped Transfer Functions (TFs), thus producing nine iBSSA variants. To verify the robustness of these variants, three well-known classification techniques, including k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest (RF) were adopted as fitness evaluators with the proposed iBSSA approach and many other competing algorithms, on 18 multifaceted, multi-scale benchmark datasets from the University of California Irvine (UCI) data repository. Then, the overall best-performing iBSSA variant for each of the three classifiers was compared with binary variants of 12 different well-known meta-heuristic algorithms, including the original SSA (BSSA), Artificial Bee Colony (BABC), Particle Swarm Optimization (BPSO), Bat Algorithm (BBA), Grey Wolf Optimization (BGWO), Whale Optimization Algorithm (BWOA), Grasshopper Optimization Algorithm (BGOA) SailFish Optimizer (BSFO), Harris Hawks Optimization (BHHO), Bird Swarm Algorithm (BBSA), Atom Search Optimization (BASO), and Henry Gas Solubility Optimization (BHGSO). Based on a Wilcoxon’s non-parametric statistical test (alpha =0.05), the superiority of iBSSA with the three classifiers was very evident against counterparts across the vast majority of the selected datasets, achieving a feature size reduction of up to 92% along with up to 100% classification accuracy on some of those datasets.

Alcoholic EEG signals recognition based on phase space dynamic and geometrical features

Alcoholism is a severe disorder that leads to brain problems and associated cognitive, emotional and behavioral impairments. This disorder is typically diagnosed by a questionnaire technique known as CAGE that measures the severity of a drinking problem. This is a time-consuming, onerous, error-prone, and biased method. Hence, this article aims to establish a novel framework for automatic detecting alcoholism using electroencephalogram (EEG) signals, which can mitigate these issues and help clinicians and researchers. In the proposed framework, firstly, we explore the phase space dynamic of EEG signals for visualizing the chaotic nature and complexity of EEG signals. Secondly, we discover thirty-four graphical features for decoding the chaotic behavior of normal and alcoholic EEG signals. After that, we investigate thirteen feature selection in combination with eleven machine learning and neural network classifiers to select the best combination for the development of an efficient framework. The experimental results reveal that the proposed method achieves the highest classification performance involving 99.16% accuracy, 100% sensitivity and 98.36% specificity for the twenty-three features selected by Henry gas solubility optimization with feedforward neural network (FFNN). The proposed system provides a new visual biomarker for alcoholic detection. In addition, we developed two new indexes using clinically relevant features to distinguish normal and alcoholic classes with a single number. These indexes can help medical teams, commercial users as well as product developers to develop a real-time system.

Novel hybrid machine learning approach for predicting structural response of RC beams under blast loading

Explosions have inflicted colossal human and economic losses. However, existing response prediction methods for blast-loaded reinforced concrete (RC) structures are complex and suffer from several limitations. The novelty of this study consists of developing a hybrid machine learning model to predict the maximum displacement of RC beams under blast loading. The model was developed using a database compiled from published studies and considers influential features of RC beams and blast parameters. Hybrid gradient-boosted regression trees algorithm coupled with Henry gas solubility optimization algorithm was developed and validated using multiple statistical metrics, feature importance analyses, and comparisons to traditional methods. A failure mode and crack pattern classification model was also developed to predict the post-blast condition of RC beams. It was scrutinized using multi-class confusion matrix and feature importance analysis. The displacement prediction model achieved superior performance with MAE of 4.48 mm and R2 of 93.4%. The classification model attained adequate performance with binary-class accuracy of 93.1% and multi-class accuracy of 83.74%. Both models captured features’ importance, while strongly correlating to experimental studies. The model opens the door for creating an improved and integrated machine learning model for predicting the quantitative and qualitative behavior of RC beams under blast loading in future research.

A novel fuzzy clustering based method for image segmentation in RGB-D images

Automatic image segmentation is a challenging task in computer vision applications, especially in the presence of occluded objects, varying color, and different lighting conditions. The advancement of depth-sensing technologies has introduced RGB-Depth cameras which are capable to generate RGB-Depth images and brought significant changes in computer vision applications. However, the segmentation of RGB-Depth images is a difficult task. Therefore, in this paper, a new segmentation method for RGB-Depth images has been introduced and named as random Henry gas solubility optimization-fuzzy clustering method. Firstly, a random Henry gas solubility optimization algorithm has been developed. Next, the proposed optimization algorithm has been employed to obtain optimal fuzzy clusters which are finally merged through segmentation by aggregating superpixels. The standard NYU depth V2 RGB-Depth indoor image dataset is used for performance evaluation. The proposed segmentation approach has been compared with five different methods namely, kmeans, fuzzy c-means, Henry gas solubility optimization algorithm, chaotic gravitational search algorithm, and J-Segmentation in terms of qualitative and quantitative measures. The result analysis shows that the proposed RGB-D segmentation method outperforms the other considered methods.

An improved Henry gas solubility optimization algorithm based on Lévy flight and Brown motion

An improved Henry gas solubility optimization algorithm based on Lévy flight and Brown motion

Improved Binary Adaptive Wind Driven Optimization Algorithm-Based Dimensionality Reduction for Supervised Classification

Typically, Feature Selection (FS) is adopted as a critical preprocessing step in most pattern recognition and data mining tasks. It helps to avoid the acute impact of irrelevant and redundant features on the performance of the classification model under consideration. To tackle this problem, researchers have proposed different methods for selecting the most significant features to improve the overall classification accuracy for a given dataset by extracting relevant information. Realistically, for a given dataset with a large number of features, conventional methods usually struggle to find good solutions. Therefore, in this study, a meta-heuristic algorithm called the Wind Driven Optimization (WDO) is enhanced and then cloned into a binary variant, the improved Binary Adaptive WDO (iBAWDO). The proposed iBAWDO would manage to select the most relevant (near-optimal) features while reducing the computational cost and enhancing (or even maintaining) the final classification accuracy. An evolutionary crossover technique as well as the Simulated Annealing algorithm (SA) are incorporated into the original WDO algorithm to enhance its search ability for feasible regions as well as exploitation within these regions, respectively. To assess the relevance of the features selected, two popular classifiers, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM), are adopted as fitness evaluators. The proposed iBAWDO algorithm was validated on 18 multi-scale benchmark datasets against binary versions of 11 well-known meta-heuristic approaches: Binary version of the original WDO (BWDO), Binary Particle Swarm Optimization (BPSO), Binary Bat Algorithm (BBA), Binary Grey Wolf Optimization (BGWO), Binary Whale Optimization Algorithm (BWOA), Binary Grasshopper Optimization Algorithm (BGOA), Binary Sailfish Optimizer (BSFO), Binary Harris Hawks optimization (BHHO), Binary Bird Swarm Algorithm (BBSA), Binary Atom Search Optimization (BASO), and Binary Henry Gas Solubility Optimization (BHGSO). A Wilcoxon’s rank-sum non-parametric test was conducted at a 5% significance level to statistically affirm the high competitiveness of the proposed method. Overall, the experimental results revealed that the proposed method is significantly effective on both small- and large-dimensional datasets.

Random Forest-Based Oppositional Henry Gas Solubility Optimization Model for Service Attack Improvement in WSN

Random Forest-Based Oppositional Henry Gas Solubility Optimization Model for Service Attack Improvement in WSN

An automatic plant leaf disease identification using DenseNet-121 architecture with a mutation-based henry gas solubility optimization algorithm

Farmers are struggling to provide the fast-growing population with sufficient agricultural products, while plant diseases result in devastating food loss. The billions of dollars spent by agriculturists in disease management often result in poor disease control without any technical support. Advances in computer vision techniques help to detect plant pathogens at an earlier level with an adaptive algorithm designed through deep learning and machine learning techniques. In this paper, we present an efficient Mutation-based Henry Gas Solubility Optimization (MHGSO) algorithm to optimize the hyperparameters of the DenseNet-121 architecture. The hyperparameter optimization is mainly done to reduce the computational complexity and the error rate of the Convolutional Neural Network (CNN). This step helps the MHGSO optimized DenseNet-121 architecture to achieve a higher classification accuracy for classifying different plant images from the PlantVillage dataset. The experimental results achieved showed that the proposed model is capable of classifying 14 leaf classes present in the PlantVillage dataset with higher classification accuracy (98.7%) and stability. When tested with a field dataset with complicated backgrounds, the proposed MHGSO optimized DenseNet-121 architecture achieves accuracy, precision, and recall scores of 98.81%, 98.60%, and 98.75%, respectively.

An improved RUN optimizer based real PID plus second-order derivative controller design as a novel method to enhance transient response and robustness of an automatic voltage regulator

It is crucial to maintain the terminal voltage of a power network in order to reach a desired power quality. In this regard, a system called automatic voltage regulator (AVR) holds a significant role as it allows the adjustment of the terminal voltage. However, an AVR system requires an efficient control technique in order to provide stability. Considering this requirement, a new method is presented in this paper for transient response enhancement and robustness of the automatic voltage regulator (AVR) system. In that sense, the structure of a recently developed metaheuristic algorithm named Runge Kutta optimizer (RUN) is improved. The improved RUN (iRUN) optimizer is obtained by integrating the pattern search (PS) method into the original form of the RUN optimizer. The constructed iRUN optimizer is proposed as an efficient tool to tune a real proportional-integral-derivative (PID) plus second-order derivative (PIDD2) controller employed in an AVR system. In the proposed control mechanism, a performance index known as ZLG is used as an objective function. The proposed iRUN optimizer based real PIDD2 controller is comparatively demonstrated to be superior in terms of transient response and robustness against other available and recently reported methods based on PID, PID with filter (PID-F), PID acceleration (PIDA), fractional order PID (FOPID) and PID plus second-order derivative (PIDD2) controllers that are respectively tuned with metaheuristic optimizers of stochastic fractal search algorithm, Archimedes optimization algorithm, whale optimization algorithm, Henry gas solubility optimization and particle swarm optimization. The proposed method is shown to achieve no overshot and less rise and settling times compared to listed methods. It is also shown to be sufficiently robust in case of potential variations that may occur in the parameters of the system. The superiority is further demonstrated using thirty good performing and recently reported different methods that adopt different controllers and metaheuristic optimizers, as well. All comparative analyses confirm the excellent capability of the proposed iRUN optimizer based real PIDD2 controller for the AVR system control.

CNN Features and Optimized Generative Adversarial Network for COVID-19 Detection from Chest X-Ray Images.

Coronavirus is a RNA type virus, which makes various respiratory infections in both human as well as animals. In addition, it could cause pneumonia in humans. The Coronavirus affected patients has been increasing day to day, due to the wide spread of diseases. As the count of corona affected patients increases, most of the regions are facing the issue of test kit shortage. In order to resolve this issue, the deep learning approach provides a better solution for automatically detecting the COVID-19 disease. In this research, an optimized deep learning approach, named Henry gas water wave optimization-based deep generative adversarial network (HGWWO-Deep GAN) is developed. Here, the HGWWO algorithm is designed by the hybridization of Henry gas solubility optimization (HGSO) and water wave optimization (WWO) algorithm. The pre-processing method is carried out using region of interest (RoI) and median filtering in order to remove the noise from the images. Lung lobe segmentation is carried out using U-net architecture and lung region extraction is done using convolutional neural network (CNN) features. Moreover, the COVID-19 detection is done using Deep GAN trained by the HGWWO algorithm. The experimental result demonstrates that the developed model attained the optimal performance based on the testing accuracy of 0.9169, sensitivity of 0.9328, and specificity of 0.9032.

Optimal Fusion-Based Handcrafted with Deep Features for Brain Cancer燙lassification

Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography (CT), or magnetic resonance imaging (MRI). An automated brain cancer classification using computer aided diagnosis (CAD) models can be designed to assist radiologists. With the recent advancement in computer vision (CV) and deep learning (DL) models, it is possible to automatically detect the tumor from images using a computer-aided design. This study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features (HGSO-FHDF) technique for brain cancer classification. The proposed HGSO-FHDF technique aims for detecting and classifying different stages of brain tumors. The proposed HGSO-FHDF technique involves Gabor filtering (GF) technique for removing the noise and enhancing the quality of MRI images. In addition, Tsallis entropy based image segmentation approach is applied to determine injured brain regions in the MRI image. Moreover, a fusion of handcrafted with deep features using Residual Network (ResNet) is utilized as feature extractors. Finally, HGSO algorithm with kernel extreme learning machine (KELM) model was utilized for identifying the presence of brain tumors. For examining the enhanced brain tumor classification performance, a comprehensive set of simulations take place on the BRATS 2015 dataset.

Fault Diagnosis of Aeroengine Control System Sensor Based on Optimized and Fused Multidomain Feature

Improving the efficacy and dependability of aeroengines requires timely and effective sensor fault diagnosis. Deep learning-based fault diagnosis method is a current research hotspot. To overcome some of the method’s existing shortcomings and improve the reliability of fault diagnosis, this paper proposes a novel intelligent fault diagnosis framework with higher quality features and more effective fault classifiers. The proposed plan includes three stages. Firstly, multidomain features (time and frequency domain features) are extracted to describe the sensor’s health from several dimensions. Secondly, the advanced Henry gas solubility optimization algorithm (HGSO) is applied to improve classification accuracy through feature selection, and the operating conditions and the features extracted by the network are fused as fault indicators. Finally, an adaptive deep belief network (ADBN) with relu-softsign combination activation layers, variable learning rate, and optimized network structure is proposed as the fault identifier. The advantages of the first two stages lie in the complete utilization of information and reducing the data dimension. In addition, the detection performance and the convergence speed is enhanced by the proposed ADBN. The experimental data are derived from a combination of measured and simulated data generated from the aeroengine model. The experimental results indicate that the improved method can produce better performance and outcomes than the unimproved methods for all fault scenarios, with a higher diagnostic accuracy of 98.1% and a reduced time of 98 s. The efforts of this study provide a efficient and adaptable way to aeroengine sensor fault diagnosis.

Hybrid Henry Gas Solubility Optimization: An Effective Algorithm for Fuel Consumption Vehicle Routing Problem

The depletion of non-renewable fuel reserves is the biggest problem in the logistics sector. This problem encourages the transportation sector to increase fuel efficiency in distribution activities. The fuel optimization problem in distribution routing problems is called the Fuel Consumption Vehicle Routing Problem (FCVRP). This study proposes a novel Hybrid Henry Gas Solubility Optimization (HHGSO) to solve FCVRP problems. Experiments with several parameter variants were carried out to determine the performance of HHGSO in optimizing fuel consumption. The results show that the parameters of the HHGSO algorithm affect fuel consumption and computation time. In addition, the higher the KPL, the smaller the resulting fuel consumption. The proposed algorithm is also compared with several algorithms. The comparison results show that the proposed algorithm produces better computational time and fuel consumption than the Hybrid Particle Swarm Optimization and Tabu Search algorithms.

Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization

Estimating the price of crude oil, which is seen as an important resource for economic development and stability in the world, is a topic of great interest by policy makers and market participants. However, the chaotic and nonlinear characteristics of crude oil time series (COTS) make it difficult to estimate crude oil prices with high accuracy. To overcome these challenges, a new crude oil price prediction model is proposed in this study, which includes the long short-term memory (LSTM), technical indicators such as trend, volatility and momentum, and the chaotic Henry gas solubility optimization (CHGSO) technique. In the proposed model, features based on trend, momentum and volatility technical indicators are utilized. The features are obtained by using the trend indicators such as exponential moving average (EMA), simple moving average (SMA) and Kaufman's adaptive moving average (KAMA), the momentum indicators such as commodity channel index (CCI), rate of change (ROC) and relative strength index (RSI), and the volatility indicators such as average true range (ATR), volatility ratio (VR) and highest high-lowest low (HHLL). These indicators are obtained separately for the West Texas Intermediate (WTI) and Brent COTS. Especially, including the volatility indicator in the model is important in terms of the robustness of the proposed model. The features based on EMA, SMA, KAMA indicators are composed by changing the period values between 3 and 10, the features based on ROC indicator is created by changing the period values between 5 and 12, and the features based on CCI, RSI, ATR, VR and HHLL indicators are formed by changing the period values between 5 and 20. The features are selected by CHGSO algorithm based on the logistic chaotic map, which is successful in avoiding local optima and balancing exploitation and exploration in the search space. Both Theil's U and the mean absolute percentage error (MAPE) values are utilized in the optimization algorithm as the objective function. The results show that the proposed prediction model copes with the chaoticity and nonlinear dynamics of both WTI and Brent COTS.

A multi-external archive-guided Henry Gas Solubility Optimization algorithm for solving multi-objective optimization problems

This paper proposes a new multi-objective evolutionary algorithm by adapting the recent Henry Gas Solubility Optimization (HGSO) with multiple objectives. The proposed MOHGSO uses the Pareto dominance relation as means of comparison and integrates two types of archive, while an elite archive is used to store the Pareto solutions found over the evolutionary process, the other external-archives are used to store the local best solutions corresponding to each cluster. Moreover, efficient archiving and leader selection strategies based on the crowding distance computation are proposed to guide the population towards the true Pareto front. The performance of the MOHGSO algorithm is validated through an extensive comparison with three well-known algorithms on twelve test functions and four engineering design problems. The experiments of two widely used metrics in the field called IGD and Sp metrics show the ability of the proposed algorithm in achieving interesting results. Furthermore, the statistical results related to the Wilcoxon test indicate that the proposed algorithm outperforms significantly the selected methods for the above metrics at a 99% confidence level.

Predicting roof displacement of roadways in underground coal mines using adaptive neuro-fuzzy inference system optimized by various physics-based optimization algorithms

Due to the rapid industrialization and the development of the economy in each country, the demand for energy is increasing rapidly. The coal mines have to pace up the mining operations with large production to meet the energy demand. This requirement has led underground coal mines to go deeper with more difficult conditions, especially the mining hazards, such as large deformations, rockburst, coal burst, roof collapse, to name a few. Therefore, this study aims at investigating and predicting the stability of the roadways in underground coal mines exploited by longwall mining method, using various novel intelligent techniques based on physics-based optimization algorithms (i.e. multi-verse optimizer (MVO), equilibrium optimizer (EO), simulated annealing (SA), and Henry gas solubility optimization (HGSO)) and adaptive neuro-fuzzy inference system (ANFIS), named as MVO-ANFIS, EO-ANFIS, SA-ANFIS and HGSO-ANFIS models. Accordingly, 162 roof displacement events were investigated based on the characteristics of surrounding rocks, such as cohesion, Young's modulus, density, shear strength, angle of internal friction, uniaxial compressive strength, quench durability index, rock mass rating, and tensile strength. The MVO-ANFIS, EO-ANFIS, SA-ANFIS and HGSO-ANFIS models were then developed and evaluated based on this dataset for predicting roof displacements in roadways of underground mines. The results indicated that the proposed intelligent techniques could accurately predict the roof displacements in roadways of underground mines with an accuracy in the range of 83%–92%. Remarkably, the SA-ANFIS model yielded the most dominant accuracy (i.e. 92%). Based on the accurate predictions from the proposed techniques, the reinforced solutions can be timely suggested to ensure the stability of roadways during exploiting coal, especially in the underground coal mines exploited by the longwall mining. • Roof displacements in underground coal mines were investigated. • Physics-based optimization algorithms were applied in this regard. • ANFIS was applied to determine the relationship of surrounding rocks. • Four AI techniques were proposed to predict roof displacements. • The SA-ANFIS model provided the highest accuracy in predicting roof displacements.

RETRACTED ARTICLE: Artificial intelligence assisted Internet of Things based financial crisis prediction in FinTech environment

AbstractFinancial Technology (FinTech) is treated as a distinctive taxonomy which majorly examines the financial technology sectors in a broader set of operations for enterprises by the use of Information Technology (IT) applications. Since the Internet of Things (IoT) is increasing tremendously, artificial intelligence (AI) assisted agile IoT is the way forward for sustainable finance. The deepness of the agile IoT has probably transformed the financial market today, and it may rapidly develop as a dominant tool in the future. The integration of AI and IoT techniques will considerably extract valued financial data and avail better services to the customers. One of the important concepts involved in FinTech is financial crisis prediction (FCP), which is a process of determining the financial status of a company. With this motivation, this paper designs a novel artificial intelligence assisted IoT based FCP (AIAIoT-FCP) model in the FinTech environment. The proposed AIAIoT-FCP model encompasses different stages such as data collection, data preprocessing, feature selection, and classification. At the primary stage, the financial data of the enterprises are collected by the use of the IoT devices such as smartphones and laptops. Besides, a chaotic Henry gas solubility optimization based feature selection (CHGSO-FS) technique is applied to select optimum features. In addition, a deep extreme learning machine (DELM) based classifier is used to determine the class labels of the financial data. Finally, the Nesterov-accelerated Adaptive Moment Estimation (NADAM) based hyperparameter optimizer of the DELM model is involved to boost the classification performance of the DELM model. An extensive simulation analysis is carried out on the benchmark financial dataset to highlight the betterment of the AIAIoT-FCP model. The resultant values portrayed the superior performance of the AIAIoT-FCP model over the state of art techniques in a considerable way.

Henry gas solubility optimization for control of a nuclear reactor: A case study

Meta-heuristic algorithms have found their place in optimization problems. Henry gas solubility optimization (HGSO) is one of the newest population-based algorithms. This algorithm is inspired by Henry's law of physics. To evaluate the performance of a new algorithm, it must be used in various problems. On the other hand, the optimization of the proportional–integral–derivative (PID) gains for load-following of a nuclear power plant (NPP) is a good challenge to assess the performance of HGSO. Accordingly, the power control of a pressurized water reactor (PWR) is targeted, based on the point kinetics model with six groups of delayed-neutron precursors. In any optimization problem based on meta-heuristic algorithms, an efficient objective function is required. Therefore, the integral of the time-weighted square error (ITSE) performance index is utilized as the objective (cost) function of HGSO, which is constrained by a stability criterion in steady-state operations. A Lyapunov approach guarantees this stability. The results show that this method provides superior results compared to an empirically tuned PID controller with the least error. It also achieves good accuracy compared to an established GA-tuned PID controller.

Combined heat and power plant using a multi-objective Henry gas solubility optimization algorithm: A thermodynamic investigation of energy, exergy, and economic (3E) analysis

The principal context of this study was a combined heat and power plant (CHPP) system, with the aim of conducting the multi-objective optimization (MOO) of an energy, exergy, and economic (3E) analysis. To meet rising energy demands, optimal operational conditions for CHPPs are required. Enhancements to plant equipment and improvements in plant design are critical. CHPP design has its basis in the first law of thermodynamics; the losses from such systems are therefore most accurately determined via exergy analysis. Energy quality can also be assessed using exergy analysis. Consequently, it is possible for the designers of thermodynamic systems to apply the findings to achieve improved efficiencies. The economic aspect of CHPP optimization is also critical because the structure is highly complex. This study therefore makes use of a Henry gas solubility optimization (HGSO) algorithm in a CHPP base case situation to achieve MOO. In this particular CHPP system, the respective enthalpy and exergy efficiencies were increased in the case of the boiler (7.22% and 7.21%), the turbogenerator (4.52% and 6.84%), and the condenser (3.06% and 31.37%). In this study, four scenarios are proposed, whereby the design of a heat exchanger network (HEN) aims to optimize energy savings and economic performance through analysis of the profits generated through electricity and steam production. A payback period of around two to three years was reported, where the cost increase under optimal conditions was found to be 0.3824%. The results demonstrate clearly that the tested techniques may be appropriate in practical scenarios when enhancing CHPP performance in the context of the base case.