Modeling of an Automatic Optimization System of Cyanide Concentration in Carbon in Leach for Optimal Ore Processing in a Mining Company

The optimization system, which was the subject of our study, is an autonomous chain for the automatic management of cyanide consumption. It is in the phase of industrial automation which made it possible to use the machines in order to reduce the workload of the worker while keeping a high productivity and a quality in great demand. Furthermore, the use of cyanide in leaching tanks is a necessity in the gold recovery process. This consumption of cyanide must be optimal in these tanks in order to have a good recovery while controlling the concentration of cyanide. Cyanide is one of the most expensive products for mining companies. On a completely different note, we see huge variations during the addition of cyanide. Following a recommendation from the metallurgical and operations teams, the control team carried out an analysis of the problem while proposing a solution to reduce the variability around plus or minus 10% of the addition setpoint through automation. It should be noted that this automatic optimization by monitoring the concentration of cyanide, made use of industrial automation which is a technique which ensures the operation of the ore processing chain without human intervention. In other words, it made it possible to substitute a machine for man. So, this leads us to conduct a study on concentration levels in the real world. The results show that the analysis of the modeling of the cyanide consumption optimization system is an appropriate solution to eradicate failures in the mineral processing chain. The trend curves demonstrate this resolution perfectly.

Machine learning-driven optimization of plasma-catalytic dry reforming of methane

This study investigates the dry reformation of methane(DRM)over Ni/Al_(2)O_(3)catalysts in a dielectric barrier discharge(DBD)non-thermal plasma reactor.A novel hybrid machine learning(ML)model is developed to optimize the plasma-catalytic DRM reaction with limited experimental data.To address the non-linear and complex nature of the plasma-catalytic DRM process,the hybrid ML model integrates three well-established algorithms:regression trees,support vector regression,and artificial neural networks.A genetic algorithm(GA)is then used to optimize the hyperparameters of each algorithm within the hybrid ML model.The ML model achieved excellent agreement with the experimental data,demonstrating its efficacy in accurately predicting and optimizing the DRM process.The model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM performance.We found that the optimal discharge power(20 W),CO_(2)/CH_(4)molar ratio(1.5),and Ni loading(7.8 wt%)resulted in the maximum energy yield at a total flow rate of∼51 mL/min.Furthermore,we investigated the relative significance of each operating parameter on the performance of the plasma-catalytic DRM process.The results show that the total flow rate had the greatest influence on the conversion,with a significance exceeding 35%for each output,while the Ni loading had the least impact on the overall reaction performance.This hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets,enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.

Advancements in machine learning for material design and process optimization in the field of additive manufacturing

Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.

Optimization of Hydrocracking Process for Enhanced BTX Production from Coal Tar-Derived Hydrorefined Products

Hydroconversion of coal tar to produce aromatic hydrocarbons(BTX)represents a crucial strategy for the highvalue hierarchical utilization of coal.This study focused on the hydrocracking of hydrorefined products derived from coal tar to enhance the production of benzene,toluene,and xylene(BTX).Various reaction conditions,including reaction temperature,hydrogen pressure,space velocity,and hydrogen-to-oil volume ratio,were systematically explored to optimize BTX yields while also considering the process’s economic feasibility.The results indicate that increasing the reaction temperature from 360℃ to 390℃ significantly favors the production of BTX,with yields increasing from 21.42%to 41.14%.Similarly,an increase in hydrogen pressure from 4 MPa to 6 MPa boosts BTX production,with yields rising from 36.31%to 41.14%.Reducing the space velocity from 2 h^(-1) to 0.5 h^(-1) also favors the BTX production process,with yields increasing from 37.96%to 45.13%.Furthermore,raising the hydrogen-to-oil volume ratio from 750 to 1500 improves BTX yields from 41.61%to 45.44%.Through economic analysis,the optimal conditions for BTX production were identified as a reaction temperature of 390℃,hydrogen pressure of 5-6 MPa,space velocity of 1 h^(-1),and hydrogen-to-oil volume ratio of 1000,achieving a BTX yield of 43.73%.This investigation highlights the importance of a holistic evaluation of hydrocracking conditions to optimize BTX production.Furthermore,the findings offer valuable insights for the design and operation of industrial hydrocracking processes aimed at efficiently converting coal tar-derived hydrorefined feedstock into BTX.

Design and optimization of a greener sinomenine hydrochloride preparation process considering variations among different batches of the medicinal herb

The current methods used to industrially produce sinomenine hydrochloride involve several issues,including high solvent toxicity,long process flow,and low atomic utilization efficiency,and the greenness scores of the processes are below 65 points.To solve these problems,a new process using anisole as the extractant was proposed.Anisole exhibits high selectivity for sinomenine and can be connected to the subsequent water-washing steps.After alkalization of the medicinal material,heating extraction,water washing,and acidification crystallization were carried out.The process was modeled and optimized.The design space was constructed.The recommended operating ranges for the critical process parameters were 3.0–4.0 h for alkalization time,60.0–80.0℃ for extraction temperature,2.0–3.0(volume ratio)for washing solution amount,and 2.0–2.4 mol·L^(-1) for hydrochloric acid concentration.The new process shows good robustness because different batches of medicinal materials did not greatly impact crystal purity or sinomenine transfer rate.The sinomenine transfer rate was about 20%higher than that of industrial processes.The greenness score increased to 90 points since the novel process proposed in this research solves the problems of long process flow,high solvent toxicity,and poor atomic economy,better aligning with the concept of green chemistry.

Optimization and Characterization of Combined Degumming Process of Typha angustata L. Stem Fibers

Plant derived natural fibers have been widely investigated as alternatives to synthetic fibers in reinforcing polymers.Researchers over the years have explored many plant fibers using different extraction processes to study their physical,chemical,and mechanical properties.In this context,the present study relates to the extraction,characterization,and optimization of Typha angustata L.stem fibers.For this purpose,desirability functions and response surface methodology were applied to simultaneously optimize the diameter(D),linear density(LD);yield(Y),lignin fraction(L),and tenacity(T)of Typha stem fibers.Typha stems have been subjected to both alkali(NaOH)and enzymatic(pectinex ultra-SPL)treatments.Three levels of process variables including enzyme concentration(10,15,and 20 ml/L)and treatment duration(10,15,and 20 days)were used to design the experiments according to the factorial design.Experimental results were examined by analysis of variance and fitted to second order polynomial model using multiple regression analysis.The Derringer’s desirability function released that the values of process variables generating optimized diameter,linear density,yield,lignin ratio and tenacity are 20 ml/L and 20 days for concentration of pectinex ultra-SPL enzyme and treatment duration,respectively.Confirmation was performed and high degree of correlation was found between the experimental and statistical values.Moreover,the morphological structure has been investigated by the scanning electron microscope,showing a crenelated structure of ultimate fiber bundles of cellulose composing the Typha fiber.Compared to Typha stem non-treated fibers(TSNTF),Typha stem combined treated fibers(TSCTF),brings to improve mechanical properties.This change in mechanical properties is affected by modifying the fiber structure showing alpha cellulose of(66.86%)and lignin ratio of(10.83%)with a crystallinity index of(58.47%).

Cookie Baking Process Optimization and Quality Analysis Based on Food 3D Printing

In order to obtain better quality cookies, food 3D printing technology was employed to prepare cookies. The texture, color, deformation, moisture content, and temperature of the cookie as evaluation indicators, the influences of baking process parameters, such as baking time, surface heating temperature and bottom heating temperature, on the quality of the cookie were studied to optimize the baking process parameters. The results showed that the baking process parameters had obvious effects on the texture, color, deformation, moisture content, and temperature of the cookie. All of the roasting surface heating temperature, bottom heating temperature and baking time had positive influences on the hardness, crunchiness, crispiness, and the total color difference(ΔE) of the cookie. When the heating temperatures of the surfac and bottom increased, the diameter and thickness deformation rate of the cookie increased. However,with the extension of baking time, the diameter and thickness deformation rate of the cookie first increased and then decreased. With the surface heating temperature of 180 ℃, the bottom heating temperature of 150 ℃, and baking time of 15 min, the cookie was crisp and moderate with moderate deformation and uniform color. There was no burnt phenomenon with the desired quality. Research results provided a theoretical basis for cookie manufactory based on food 3D printing technology.

Optimization of the Pretreatment of the Mixture of Cassava Peelings and Pineapple Fibers Using Response Surface Methodology and a Process Simulator for the Bioethanol Production

The increase in oil prices and greenhouse gas emissions has led to the search for substitutes for fossil fuels. In Cameroon, the abundance of lignocellulosic resources is inherent to agricultural activity. Production of bioethanol remains a challenge given the crystallinity of cellulose and the presence of the complex. The pretreatment aimed to solubilize the lignin fraction and to make cellulose more accessible to the hydrolytic enzymes, was done using the organosolv process. A mathematical modeling was performed to point out the effect of the temperature on the kinetics of the release of the reducing sugars during the pretreatment. Two mathematical model was used, SAEMAN’s model and Response surface methodology. The first show that the kinetic parameters of the hydrolysis of the cellulose and reducing sugar are: 0.05089 min-1, 5358.1461 J·mol-1, 1383.03691 min-1, 51577.6100 J·mol-1 respectively. The second model was used. Temperature is the factor having the most positive influence whereas, ethanol concentration is not an essential factor. To release the maximum, an organosolv pre-treatment of this sub-strate should be carried out at 209.08°C for 47.60 min with an ethanol-water ratio of 24.02%. Organosolv pre-treatment is an effective process for delignification of the lignocellulosic structure.

Mechanism of Rosae Rugosae Flos flavonoids in the treatment of hyperlipidemia and optimization of extraction process based on network pharmacology

This study aims to identify a natural plant chemical with hypolipidemic effects that can be used to treat high cholesterol without adverse reactions.Through network pharmacology screening,it was found that Rosae Rugosae Flos(RF)flavonoids had potential therapeutic effects on hyperlipidemia and its mechanism of action was discussed.TCMSP and GeneCards databases were used to obtain active ingredients and disease targets.Venn diagrams were drawn to illustrate the findings.The interaction network diagram was created using Cytoscape 3.8.0 software.The PPI protein network was constructed using String.GO and KEGG enrichment analysis was performed using Metascape.The results revealed 2 active flavonoid ingredients and 60 potential targets in RF.The key targets,including CCL2,PPARG,and PPARA,were found to play a role in multiple pathways such as the AGE-RAGE signaling pathway,lipid and atherosclerosis,and cancer pathway in diabetic complications.The solvent extraction method was optimized for efficient flavonoid extraction based on network pharmacology prediction results.This was achieved through a single factor and orthogonal test,resulting in an optimum process with a reflux time of 1.5 h,a solid-liquid ratio of 1:13 g/mL,and an ethanol concentration of 50%.

Design and optimization of purification process of sinomenine hydrochloride

Sinomenine hydrochloride is generally produced from Caulis Sinomenii. At present, the purification process in industrial production suffers from large amount of solid waste, high solvent toxicity, and low sinomenine hydrochloride yield. In this study, a new purification process for sinomenine hydrochloride was proposed by using the extract obtained from acid extraction of Caulis Sinomenii as the starting material.The process included the following steps: alkalization, extraction, water washing, acid–water stripping,drying, and crystallization. 1-Heptanol was used as the extractant. The distribution coefficients of sinomenine and sinomenine hydrochloride in 1-heptanol–water system were 27.4 and 0.0167, respectively.The dissociation constants of sinomenine hydrochloride were 8.27 and 11.24, respectively. Process parameters of the new purification process were optimized with experimental design. The extractant1-heptanol and sinomenine hydrochloride in the crystallization mother solution can be recycled in the new process. The purity of the obtained sinomenine hydrochloride crystals exceeded 85%, and the yield was about 70%. Compared with current industrial processes, safer extractant, less solid waste, and higher sinomenine hydrochloride yield can be achieved using the new purification process of sinomenine hydrochloride provided in this study.

Multi-objective optimization of process parameters for ultra-narrow gap welding based on Universal Kriging and NSGA Ⅱ

The successful confinement of the arc by the flux band depends on the welding process parameters for achieving single-pass,multi-layer, and ultra-narrow gap welding. The sidewall fusion depth, the width of the heat-affected zone, and the line energy are utilized as comprehensive indications of the quality of the welded joint. In order to achieve well fusion and reduce the heat input to the base metal.Three welding process characteristics were chosen as the primary determinants, including welding voltage, welding speed, and wire feeding speed. The metamodel of the welding quality index was built by the orthogonal experiments. The metamodel and NSGA-Ⅱ(Non-dominated sorting genetic algorithm Ⅱ) were combined to develop a multi-objective optimization model of ultra-narrow gap welding process parameters. The results showed that the optimized welding process parameters can increase the sidewall fusion depth, reduce the width of the heataffected zone and the line energy, and to some extent improve the overall quality of the ultra-narrow gap welding process.

Optimization of Finishing Process and Energy Savings in Denim Textile Facility

Denim is widely accepted among exported textile products due to its aesthetics, appearance, and fashion. Practitioners employed several physical or chemical treatments to improve denim qualities in denim finishing operations. So, several treatment processes, including enzymatic, bleaching, singeing, heat set, and ozone finish, are used, which made this processing more energy consumption and time-consuming. Therefore, it is significant to investigate how changing the chemicals and raw ingredients could improve the finishing process, which is environmentally and economically beneficial for sustainable production practices in the denim finishing process. This study’s research design comprises an experimental investigation in a denim plant in Bangladesh. Two different fabrics were chosen to analyze, determining the potential savings of finishing on the denim fabrics’ performance characteristics. By deducting singeing and heat-set processes, the researchers ran an experimental process by maintaining the same length of fabric. Then, the impacts of finishing process optimization on the mechanical, thermal, and comfort parameters of drape, stiffness, and tear strength were examined. The study’s findings demonstrated that this experiment increased productivity and reduced the finishing unit’s energy consumption without compromising the denim fabrics’ quality. This study significantly impacts environmental sustainability by preserving limited energy resources and manufacturing denim finishing processes.

B^(2)C^(3)NetF^(2):Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection

Currently,the improvement in AI is mainly related to deep learning techniques that are employed for the classification,identification,and quantification of patterns in clinical images.The deep learning models show more remarkable performance than the traditional methods for medical image processing tasks,such as skin cancer,colorectal cancer,brain tumour,cardiac disease,Breast cancer(BrC),and a few more.The manual diagnosis of medical issues always requires an expert and is also expensive.Therefore,developing some computer diagnosis techniques based on deep learning is essential.Breast cancer is the most frequently diagnosed cancer in females with a rapidly growing percentage.It is estimated that patients with BrC will rise to 70%in the next 20 years.If diagnosed at a later stage,the survival rate of patients with BrC is shallow.Hence,early detection is essential,increasing the survival rate to 50%.A new framework for BrC classification is presented that utilises deep learning and feature optimization.The significant steps of the presented framework include(i)hybrid contrast enhancement of acquired images,(ii)data augmentation to facilitate better learning of the Convolutional Neural Network(CNN)model,(iii)a pre‐trained ResNet‐101 model is utilised and modified according to selected dataset classes,(iv)deep transfer learning based model training for feature extraction,(v)the fusion of features using the proposed highly corrected function‐controlled canonical correlation analysis approach,and(vi)optimal feature selection using the modified Satin Bowerbird Optimization controlled Newton Raphson algorithm that finally classified using 10 machine learning classifiers.The experiments of the proposed framework have been carried out using the most critical and publicly available dataset,such as CBISDDSM,and obtained the best accuracy of 94.5%along with improved computation time.The comparison depicts that the presented method surpasses the current state‐ofthe‐art approaches.

Optimization of the Formulation Process of Glucosamine Chondroitin Sulfate Tablets

[Objectives]This study was conducted to optimize the Formulation Process of glucosamine chondroitin sulfate tablets. [Methods] The orthogonal design with three levels was carried out with microcrystalline cellulose, calcium hydrophosphate and cross-linked polyvinylpyrrolidone as three factors to optimize the preparation process. [Results] When microcrystalline cellulose 200 mg/tablet, calcium hydrophosphate 150 mg/tablet, and cross-linked polyvinylpyrrolidone 80 mg/tablet were added, the angle of repose could meet the requirements of tablet pressing, and the dissolution could reach more than 95% in 30 min. The results of the orthogonal test showed that the dissolution effect of self-made tablets was faster than that of commercial products. [Conclusions] The glucosamine hydrochloride chondroitin sulfate tablets prepared by this prescription have better quality.

Influence of layer thickness on formation quality,microstructure,mechanical properties,and corrosion resistance of WE43 magnesium alloy fabricated by laser powder bed fusion

Laser powder bed fusion(L-PBF)of Mg alloys has provided tremendous opportunities for customized production of aeronautical and medical parts.Layer thickness(LT)is of great significance to the L-PBF process but has not been studied for Mg alloys.In this study,WE43 Mg alloy bulk cubes,porous scaffolds,and thin walls with layer thicknesses of 10,20,30,and 40μm were fabricated.The required laser energy input increased with increasing layer thickness and was different for the bulk cubes and porous scaffolds.Porosity tended to occur at the connection joints in porous scaffolds for LT40 and could be eliminated by reducing the laser energy input.For thin wall parts,a large overhang angle or a small wall thickness resulted in porosity when a large layer thicknesses was used,and the porosity disappeared by reducing the layer thickness or laser energy input.A deeper keyhole penetration was found in all occasions with porosity,explaining the influence of layer thickness,geometrical structure,and laser energy input on the porosity.All the samples achieved a high fusion quality with a relative density of over 99.5%using the optimized laser energy input.The increased layer thickness resulted to more precipitation phases,finer grain sizes and decreased grain texture.With the similar high fusion quality,the tensile strength and elongation of bulk samples were significantly improved from 257 MPa and 1.41%with the 10μm layer to 287 MPa and 15.12%with the 40μm layer,in accordance with the microstructural change.The effect of layer thickness on the compressive properties of porous scaffolds was limited.However,the corrosion rate of bulk samples accelerated with increasing the layer thickness,mainly attributed to the increased number of precipitation phases.

Optimization on Fermentation Conditions of Orange Vinegar by Box-behnken Design

The optimal process for orange vinegar was investigated in order to explore the effect of process parameters and obtain the superior process parameters.The process for orange vinegar was optimized using single-factor methodology and Box-behnken methodology, and the effect of process parameters was analyzed in the light of response surface plots. Results were as follows. The optimal process of alcohol fermentation was as follows： original sugar degree of 16%, yeast inoculum size of 5% and p H value of 3.5 through single-factor experiment. The optimal fermentation process of acetic acid optimized by response surface methodology was as follows： inoculum size of 8.52%, original alcohol content of 6.0% and p H value of 3.3, in the optimal condition the yield of acetic acid was 53.11 g/L. Single factor action and the interaction of two factors were presented on the yield of acetic acid.The effects of inoculum size and p H were extremely significant, and that of original alcohol content significant. The interaction of inoculum size and original alcohol content was non-significant, that of inoculum size and p H also non-significant. On contrast, original alcohol content and p H had significant interaction.

Effect of element fitting on composition optimization of Al-Cu-Ti amorphous alloy by mechanical alloying

In order to minimize the crystal phase in Al-Cu-Ti amorphous powder,Al65Cu35-xTix amorphous powders were optimized via ball milling through adjusting the amount of Cu and Ti elements and the ball milling time.The results show that increasing the mole fraction of Ti can decrease the amount of Al Cu2Ti,Cu9Al4,and Al2Cu intermetallics formed during the process of ball milling;and prolonging the ball milling time can reduce the element crystalline phase to almost none.The optimal composition is determined to be Al65Cu16.5Ti18.5.TiH2 forms in all selected Al65Cu35-xTix amorphous powders during the process of optimization.H atom is decomposed from toluene and reacts with Ti during ball milling,leading to the formation of TiH2.The volume fraction of TiH2 in Al65Cu16.5Ti18.5 amorphous powder is measured to be 4.30%.

Study on the green extraction of corncob xylan by deep eutectic solvent

Corn as one of the world's major food crops,its by-product corn cob is also rich in resources.However,the unreasonable utilization of corn cob often causes the environmental pollution,waste of resources and other problems.As one of the most abundant polymers in nature,xylan is widely used in food,medicine,materials and other fields.Corn cob is rich in xylan,which is an ideal raw material for extracting xylan.However,the intractable lignin is covalently linked to xylan,which increases the difficulty of xylan extraction.It has been reported that the deep eutectic solvent(DES)could preferentially dissolve lignin in biomass,thereby dissolving the xylan.Then,the xylan in the extract was separated by ethanol precipitation method.The xylan precipitate was obtained after centrifugation,while the supernatant was retained.The components of the supernatant after ethanol precipitation were separated by the rotary evaporator.The ethanol,water and DES were collected for the subsequent extraction of corn cob xylan.In this study,a novel way was provided for the green production of corn cob xylan.The DES was used to extract xylan from corn cob which was used as the raw material.The effects of solid-liquid ratio,reaction time,reaction temperature and water content of DES on the extraction rate of corn cob xylan were investigated by the single factor test.Furthermore,the orthogonal test was designed to optimize the xylan extraction process.The structure of corn cob xylan was analyzed and verified.The results showed that the optimum extraction conditions of corn cob xylan were as follows:the ratio of corn cob to DES was 1:15(g:mL),the extraction time was 3 h,the extraction temperature was 60℃,and the water content of DES was 70%.Under these conditions,the extraction rate of xylan was 16.46%.The extracted corn cob xylan was distinctive triple helix of polysaccharide,which was similar to the structure of commercially available xylan.Xylan was effectively and workably extracted from corn cob by the DES method.This study provided a new approach for high value conversion of corn cob and the clean production of xylan.

Impact of Optimizing Emergency Nursing Processes on Resuscitation Success in Patients with Acute Chest Pain

Objective:To analyze the effect of optimizing the emergency nursing process in the resuscitation of patients with acute chest pain and the impact on the resuscitation success rate.Methods:66 patients with acute chest pain received by the emergency department of our hospital from January 2022 to December 2023 were selected as the study subjects and divided into two groups according to the differences in the emergency nursing process,i.e.,33 patients receiving routine emergency care were included in the control group,and 33 patients receiving the optimization of emergency nursing process intervention were included in the observation group.Patients’resuscitation effect and satisfaction with nursing care in the two groups were compared.Results:The observation group’s consultation assessment time,reception time,admission to the start of resuscitation time,and resuscitation time were shorter than that of the control group,the resuscitation success rate was higher than that of the control group,and the incidence of adverse events was lower than that of the control group,with statistically significant differences(P<0.05);and the observation group’s satisfaction with nursing care was higher than that of the control group,with statistically significant differences(P<0.05).Conclusion:Optimization of emergency nursing process intervention in the resuscitation of acute chest pain patients can greatly shorten the rescue time and improve the success rate of resuscitation,with higher patient satisfaction.

Modeling, Simulation and Optimization of a Whole Industrial Catalytic Naphtha Reforming Process on Aspen Plus Platform

A new 18-lump kinetic model for naphtha catalytic reforming reactions is discussed. By developing this model as a user module, a whole industrial continuous catalytic reforming process is simulated on Aspen plus plat-form. The technique utilizes the strong databases, complete sets of modules, and flexible simulation tools of the Aspen plus system and retains the characteristics of the proposed kinetic model. The calculated results are in fair agreement with the actual operating data. Based on the model of the whole reforming process, the process is opti-mized and the optimization results are tested in the actual industrial unit for about two months. The test shows that the process profit increases about 1000yuan·h-1 averagely, which is close to the calculated result.