Thiourea crystal growth kinetics,mechanism and process optimization during cooling crystallization

In the cooling crystallization process of thiourea,a significant issue is the excessively wide crystal size distribution(CSD)and the abundance of fine crystals.This investigation delves into the growth kinetics and mechanisms governing thiourea crystals during the cooling crystallization process.The fitting results indicate that the crystal growth rate coefficient,falls within the range of 10^(-7)to 10^(-8)m·s^(-1).Moreover,with decreasing crystallization temperature,the growth process undergoes a transition from diffusion-controlled to surface reaction-controlled,with temperature primarily influencing the surface reaction process and having a limited impact on the diffusion process.Comparing the crystal growth rate,and the diffusion-limited growth rate,at different temperatures,it is observed that the crystal growth process can be broadly divided into two stages.At temperatures above 25℃,1/qd(qd is diffusion control index)approaches 1,indicating the predominance of diffusion control.Conversely,at temperatures below 25℃,1/qd increases rapidly,signifying the dominance of surface reaction control.To address these findings,process optimization was conducted.During the high-temperature phase(35-25℃),agitation was increased to reduce the limitations posed by bulk-phase diffusion in the crystallization process.In the low-temperature phase(25-15℃),agitation was reduced to minimize crystal breakage.The optimized process resulted in a thiourea crystal product with a particle size distribution predominantly ranging from 0.7 to 0.9 mm,accounting for 84%of the total.This study provides valuable insights into resolving the issue of excessive fine crystals in the thiourea crystallization process.

Study on trifluoromethanesulfonic acid-promoted synthesis of daidzein: Process optimization and reaction mechanism

Daidzein has been widely used in pharmaceuticals,nutraceuticals,cosmetics,feed additives,etc.Its preparation process and related reaction mechanism need to be further investigated.A cost-effective process for synthesizing daidzein was developed in this work.In this article,a two-step synthesis of daidzein(Friedel–Crafts acylation and[5+1]cyclization)was developed via the employment of trifluoromethanesulfonic acid(TfOH)as an effective promoting reagent.The effect of reaction conditions such as solvent,the amount of TfOH,reaction temperature,and reactant ratio on the conversion rate and the yield of the reaction,respectively,was systematically investigated,and daidzein was obtained in 74.0%isolated yield under optimal conditions.Due to the facilitating effect of TfOH,the Friedel–Crafts acylation was completed within 10 min at 90℃ and the[5+1]cyclization was completed within 180 min at 25℃.In addition,a possible reaction mechanism for this process was proposed.The results of the study may provide useful guidance for industrial production of daidzein on a large scale.

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.

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.

Model of Plant Productivity and a Computer System for Optimization of Agro-Technology Using the Method of Exergic Analysis

A model of a potentially effective type energy-resource-saving of optimization of agro-technologies, based on the principle subordination of synergetics, was established. There was developed computer system energy-resource-saving optimization of agricultural technologies. The main feature of crop production is provided by the plants which themselves are self-organizing organisms. This allows us to adopt the principle of subordination of synergetics as the basis of the model. The value of free energy at the input into plants, estimated by the process of photosynthesis, is equal to the value of “radiation exergy for plant growth”. Assessment of the use of radiation energy is carried out based on the energy-converting characteristics of plants, which were obtained in climate chambers under controlled conditions. We used the model based on the principle of subordination of synergetics to develop common quantitative mutually agreed definitions of the main agroecological variables: Agroclimatic and Meliorative potentials of lands, their fertility, and potential (maximum) productivity of plants under different environment conditions.

Dominating factors on well productivity and development strategies optimization in Weiyuan shale gas play, Sichuan Basin, SW China

Weiyuan shale gas play is characterized by thin high-quality reservoir thickness,big horizontal stress difference,and big productivity differences between wells.Based on integrated evaluation of shale gas reservoir geology and well logging interpretation of more than 20 appraisal wells,a correlation was built between the single well test production rate and the high-quality reservoir length drilled in the horizontal wells,high-quality reservoir thickness and the stimulation treatment parameters in over 100 horizontal wells,the dominating factors on horizontal well productivity were found out,and optimized development strategies were proposed.The results show that the deployed reserves of high-quality reservoir are the dominating factors on horizontal well productivity.In other words,the shale gas well productivity is controlled by the thickness of the high-quality reservoir,the high-quality reservoir drilling length and the effectiveness of stimulation.Based on the above understanding,the development strategies in Weiyuan shale gas play are optimized as follows:(1)The target of horizontal wells is located in the middle and lower parts of Longyi 11(Wei202 area)and Longyi 11(Wei204 area).(2)Producing wells are drilled in priority in the surrounding areas of Weiyuan county with thick high-quality reservoir.(3)A medium to high intensity stimulation is adopted.After the implementation of these strategies,both the production rate and the estimated ultimate recovery(EUR)of individual shale gas wells have increased substantially.

Intelligent production optimization method for a low pressure and low productivity shale gas well

Shale gas wells frequently suffer from liquid loading and insufficient formation pressure in the late stage of production.To address this issue,an intelligent production optimization method for low pressure and low productivity shale gas well is proposed.Based on the artificial intelligence algorithms,this method realizes automatic production and monitoring of gas well.The method can forecast the production performance of a single well by using the long short-term memory neural network and then guide gas well production accordingly,to fulfill liquid loading warning and automatic intermittent production.Combined with adjustable nozzle,the method can keep production and pressure of gas wells stable automatically,extend normal production time of shale gas wells,enhance automatic level of well sites,and reach the goal of refined production management by making production regime for each well.Field tests show that wells with production regime optimized by this method increased 15%in estimated ultimate reserve(EUR).Compared with the development mode of drainage after depletion recovery,this method is more economical and can increase and stabilize production effectively,so it has a bright application prospect.

Study on Productivity Model of Herringbone-Like Laterals Wells and Optimization of Morphological Parameters Considering Threshold Pressure Gradient in Heavy Oil Reservoirs

Compared with conventional well, herringbone-like laterals wells can increase the area of oil release, and can reduce the number of wellhead slots of platforms,?and?also can greatly improve the development efficiency. Based on threshold pressure gradient in heavy oil reservoir,?and?the applied principle of mirror reflection and superposition, the pressure distribution equation of herringbone-like laterals wells is obtained in heavy oil reservoir. Productivity model of herringbone-like laterals wells is proposed by reservoir-wellbore steady seepage. The example shows that the productivity model is great accuracy?to?predict the productivity of herringbone-like laterals wells. The model is used to analyze the branching length, branching angle, branching symmetry, branching position and spacing and their effects on productivity of herringbone-like laterals wells. The principle of optimizing the well shape of herringbone-like laterals wells is proposed.

Measuring potential food productivity for different areas in Songnen Plain based on analytic hierarchy process

The improved analytic hierachy process method was utilized in this paper,and a variety of factors influencing the food production were classified into several interrelated orderly objectives layers;based on the works above,this paper made an scientific assessment on the influencing factors of food production and production potential of the various regions in the Songnen Plain.The weights and composite indices were calculated with the method of solving weight by AHP's accumulation factor sequence evaluating data,and were processed by single-level sorting and general sorting.The result showed that,the region of Changchun had the biggest potential for improving food production,but smallest for Heihe region.The key reason for the food production discrepancy of a variety of regions in Songnen Plain is the differences in scale of production and climatic conditions,the weight coefficients of which are 0.3654 and 0.2742;however,the weight coefficients of agricultural science and technology investment is very low just 0.1703,which should be increasted in the future.

Design Process Optimization Based on Design Process Gene Mapping

The idea of genetic engineering is introduced into the area of product design to improve the design efficiency. A method towards design process optimization based on the design process gene is proposed through analyzing the correlation between the design process gene and characteristics of the design process. The concept of the design process gene is analyzed and categorized into five categories that are the task specification gene, the concept design gene, the overall design gene, the detailed design gene and the processing design gene in the light of five design phases. The elements and their interactions involved in each kind of design process gene signprocess gene mapping is drawn with its structure disclosed based on its function that process gene.

Effect of Microelements (B, Zn) on Cotton Plant’s Productivity, Its Leaf Area and Plant Height

In this article, for the first time applied in our republic, the results of the effect of nano-micro fertilizers on cotton plant are given. The results of the study of the effect of microelements on the leaf area, plant height formation and yield of cotton in the conditions of the gray soil of the Zarafshan Valley are presented. Currently, the demand for ecological and safe food is increasing not only in Uzbekistan, but also in the whole world. The solution to this problem is effective use of land and increasing the amount of macrofertilizers, and after increasing the yield per unit of area, the micronutrients in the soil will decrease more, and less attention was paid to fertilizing with trace elements. This will affect the quality of the product, as well as the decrease in productivity and salinity of the land. Therefore, it is necessary to develop environmentally friendly and more economical approaches that do not harm the environment. The best solution for this is to reduce micronutrient deficiency in combination with agrotechnical methods. One of the urgent issues is the development of the technology of applying macro- and micro-fertilizers in the appropriate proportions, convenient terms, norms and methods for the cultivation of high-quality cotton crops under the soil conditions of our republic. The optimal rate of using microelements had a positive effect on the leaf area and dry mass of cotton plants. The highest result was observed when N200P140K100 + KUPRUMHITE + NANOSEREBRO kg/ha was applied with mineral fertilizer.

Novel methodology for casting process optimization using Gaussian process regression and genetic algorithm

High pressure die casting (HPDC) is a versatile material processing method for mass-production of metal parts with complex geometries,and this method has been widely used in manufacturing various products of excellent dimensional accuracy and productivity. In order to ensure the quality of the components,a number of variables need to be properly set. A novel methodology for high pressure die casting process optimization was developed,validated and applied to selection of optimal parameters,which incorporate design of experiment (DOE),Gaussian process (GP) regression technique and genetic algorithms (GA). This new approach was applied to process optimization for cast magnesium alloy notebook shell. After being trained,using data generated by PROCAST (FEM-based simulation software),the GP model approximated well with the simulation by extracting useful information from the simulation results. With the help of MATLAB,the GP/GA based approach has achieved the optimum solution of die casting process condition settings.

Recovery of iron from copper tailings via low-temperature direct reduction and magnetic separation: process optimization and mineralogical study

Currently, the majority of copper tailings are not effectively developed. Worldwide, large amounts of copper tailings generated from copper production are continuously dumped, posing a potential environmental threat. Herein, the recovery of iron from copper tailings via low-temperature direct reduction and magnetic separation was conducted; process optimization was carried out, and the corresponding mineralogy was investigated. The reduction time, reduction temperature, reducing agent (coal), calcium chloride additive, grinding time, and magnetic field intensity were examined for process optimization. Mineralogical analyses of the sample, reduced pellets, and magnetic concentrate under various conditions were performed by X-ray diffraction, optical microscopy, and scanning electron microscopy-energy-dispersive X-ray spectrometry to elucidate the iron reduction and growth mechanisms. The results indicated that the optimum parameters of iron recovery include a reduction temperature of 1150A degrees C, a reduction time of 120 min, a coal dosage of 25%, a calcium chloride dosage of 2.5%, a magnetic field intensity of 100 mT, and a grinding time of 1 min. Under these conditions, the iron grade in the magnetic concentrate was greater than 90%, with an iron recovery ratio greater than 95%.

MICROSTRVCTURE SIMULATION AND PROCESS OPTIMIZATION OF TURBINE BLADE CASTINGS

The probabilistic modeling is applied to calculate microstructural features of the thin complex smprolloy turbine blades cast by the vacuum investment process. The random distribution, orientation and physical mechanism of the nucleation, the growth kinetics of dendrites and the columnar-to-equiaxed transition (CET) are considered.Capitalizing on these simulating schemes, the comprehensive influence of key process variables on the scale and uniformity of grains has been involved quantitatively. The validity of the modeling is confirmed by selection of the optimum process variables.

From statistical analysis to process optimization during cladding using a Nd:YAG laser

Application of statistical methods to optimize the process parameters was achieved by employing full factorial design of experiments,which was accomplished by cladding using stepwise ramped laser power.The correlations between clad geometry and dilution(clad characteristics)and the main process parameters laser power(P_(l)),cladding speed(v_(c)),the powder feed rate(m)were obtained through application of variance analysis technique(ANOVA).The obtained correlations between the main processing parameters and the clad characteristics are discussed and a statistical model was developed.The desirability investigations using the developed statistical model were performed by considering the clad geometry,aspect ratio,dilution and hardness.Optimal parameters for cladding Stellite 6 on AISI 420 steel substrate and for cladding Nucalloy 488V on S355 J2 steel substrate were obtained.The optimal processing parameters can be applied to clad other materials with similar chemical compositions.

E-Commerce Supply Chain Process Optimization Based on Whole-Process Sharing of Internet of Things Identification Technology

With in-depth development of the Internet of Things(IoT)in various industries,the informatization process of various industries has also entered the fast lane.This article aims to solve the supply chain process problem in e-commerce,focusing on the specific application of Internet of Things technology in e-commerce.Warehousing logistics is an important link in today’s e-commerce transactions.This article proposes a distributed analysis method for RFID-based e-commerce warehousing process optimization and an e-commerce supply chain management process based on Internet of Things technology.This article first introduces the advantages and disadvantages of shared IoT identification technology and the IoT resource sharing platform based on the three-layer abstract data model and representational state transfer(REST)style.Combining actual IoT applications and the characteristics of an existing platform,a REST-based IoT resource sharing platform is proposed.Combined with actual projects,a REST-based IoT resource sharing platform was built,and key technology experiments were conducted for verification.Finally,optimizing the e-commerce supply chain management process under Internet of Things technology and explaining the advantages of optimized e-commerce supply chain management are discussed.Research on this subject provides a theoretical basis for the application of the Internet of Things in e-commerce and has practical significance and practical value for managing service capabilities and service levels in e-commerce.

Explosion limits estimation and process optimization of direct propylene epoxidation with H2 and O2

Direct propylene epoxidation with H2 and O2,an attractive process to produce propylene oxide(PO),has a potential explosion danger due to the coexistence of flammable gases(i.e.,C3 H6 and H2)and oxidizer(i.e.,O2).The unknown explosion limits of the multi-component feed gas mixture make it difficult to optimize the reaction process under safe operation conditions.In this work,a distribution method is proposed and verified to be effective by comparing estimated and experimental explosion limits of more than 200 kinds of flammable gas mixture.Then,it is employed to estimate the explosion limits of the feed gas mixture,some results of which are also validated by the classic Le Chatelier’s Rule and flammable resistance method.Based on the estimated explosion limits,process optimization is carried out using commercially high and inherently safe reactant concentrations to enhance reaction performance.The promising results are directly obtained through the interface called gOPT in gPROMS only by using a simple,easy-constructed and mature packed-bed reactor,such as the PO yield of 13.3%,PO selectivity of 85.1%and outlet PO fraction of 1.8%.These results can be rationalized by indepth analyses and discussion about the effects of the decision variables on the operation safety and reaction performance.The insights revealed here could shed new light on the process development of the PO production based on the estimation of the explosion limits of the multi-component feed gas mixture containing flammable gase s,inert gas and O2,followed by process optimization.

Heat treatment process optimization for face gears based on deformation and residual stress control

In this paper, based on the principle of heat transfer and thermal elastic-plastic theory, the heat treatment process optimization scheme for face gears is proposed according to the structural characteristics of the face gear and material properties of 12Cr2Ni4 steel. To simulate the effect of carburizing and quenching process on tooth deformation and residual stress distribution, a heat treatment analysis model of face gears is established, and the microstructure, stress and deformation of face gear teeth changing with time are analyzed. The simulation results show that face gear tooth hardness increases, tooth surface residual compressive stress increases and tooth deformation decreases after heat treatment process optimization. It is beneficial to improving the fatigue strength and performance of face gears.

Stochastic Process Optimization Technique

The conventional optimization methods were generally based on a deterministic approach, since their purpose is to find out an accurate solution. However, when the solution space is extremely narrowed as a result of setting many inequality constraints, an ingenious scheme based on experience may be needed. Similarly, parameters must be adjusted with solution search algorithms when nonlinearity of the problem is strong, because the risk of falling into local solution is high. Thus, we here propose a new method in which the optimization problem is replaced with stochastic process based on path integral techniques used in quantum mechanics and an approximate value of optimal solution is calculated as an expected value instead of accurate value. It was checked through some optimization problems that this method using stochastic process is effective. We call this new optimization method “stochastic process optimization technique (SPOT)”. It is expected that this method will enable efficient optimization by avoiding the above difficulties. In this report, a new optimization method based on a stochastic process is formulated, and several calculation examples are shown to prove its effectiveness as a method to obtain approximate solution for optimization problems.

Relative Varying Dynamics Based Whole Cutting Process Optimization for Thin‑walled Parts

Thin-walled parts are typically difficult-to-cut components due to the complex dynamics in cutting process.The dynamics is variant for part during machining,but invariant for machine tool.The variation of the relative dynamics results in the difference of cutting stage division and cutting parameter selection.This paper develops a novel method for whole cutting process optimization based on the relative varying dynamic characteristic of machining system.A new strategy to distinguish cutting stages depending on the dominated dynamics during machining process is proposed,and a thickness-dependent model to predict the dynamics of part is developed.Optimal cutting parameters change with stages,which can be divided by the critical thickness of part.Based on the dynamics comparison between machine tool and thickness-varying part,the critical thicknesses are predicted by an iterative algorithm.The proposed method is validated by the machining of three benchmarks.Good agreements have been obtained between prediction and experimental results in terms of stages identification,meanwhile,the optimized parameters perform well during the whole cutting process.