Multi-Objective Optimization of Multi-Product Parallel Disassembly Line Balancing Problem Considering Multi-Skilled Workers Using a Discrete Chemical Reaction Optimization Algorithm

This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle time.Based on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is designed.To enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular individuals.The established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,respectively.Introducing a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.

Extreme learning with chemical reaction optimization for stock volatility prediction

Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.

Optimization of ISSR-PCR Reaction System and Preliminary Construction of ISSR Fingerprinting of Some Species in Bryaceae

[Objective] The aim was to provide molecular basis for the identification of species in the moss family Bryaceae by the construction of inter-simple sequence repeats （ISSR） fingerprinting. [Method] In order to seek standardizing PCR reaction set-up, an orthogonal design was used to optimize ISSR-PCR amplification system of Bryaceae in five factors （Mg2＋, dNTPs, primer, DNA template, Taq DNA polymerase） at four levels respectively. [Result] A suitable ISSR reaction system was obtained, namely： 20 μl reaction system containing 5 ng of DNA template, 0.2 μmol/L primer, 2.25 mmol/L MgCl2, 0.6 U of Taq DNA polymerase, 0.4 mmol/L dNTPs. Proper annealing temperature was found at 48-50 ℃.The above system and six ISSR-PCR primers were used for the PCR amplification of 14 samples from Bryaceae and the related species in Mniaceae. A total of 86 bands were amplified, all showed polymorphism. NJ cluster analysis showed a star-shaped cladogram. [Conclusion] The results manifested that ISSR fingerprinting could provide the appropriate degree of polymorphism at low taxonomic level, so it would be a useful tool to provide additional evidence for resolving taxonomic relationships at the species level of Bryaceae.

Study on Establishment of cpSSR Marker Technology and Optimization of Its Reaction System in Jatropha curcas Linn

[ Objective] The aim of this study was to establish the optimum cpSSR-PCR system for Jatropha curcas Linn. [ Method] cpSSR-PCR amplification system for Jatropha curcas Linn influenced by five factors including Taq DNA polymerase, Mg^2＋ , DNA template, dNTP and primer were optimized from several levels. [ Result] The optimum concentration of 20 μl reaction system was 10 × Buffer, 2.00 mmol/L Mg^2＋ , 2 U/μl Taq DNA polymerase, 0.2 mmol/L dNTP, 0.2 μmol/L primer and 35 ng/μl DNA template. [ Conclusion] The optimum annealing temperature for cpSSR-PCR reaction system is 52 ℃, and the cpSSR reaction system is steady and reproducible.

A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction

Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification models for this domain.This study proposes a chemical reaction optimization(CRO)based neuro-fuzzy network model for prediction of stock indices.The input vectors to the model are fuzzified by applying a Gaussian membership function,and each input is associated with a degree of membership to different classes.A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model.CRO was chosen because it requires fewer control parameters and has a faster convergence rate.Five statistical parameters are used to evaluate the performance of the model,and the model is validated by forecasting the daily closing indices for five major stock markets.The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior.We conducted the Deibold-Mariano test to check the statistical significance of the proposed model,and it was found to be significant.This model can be used as a promising tool for financial forecasting.

Distributed Flexible Job-Shop Scheduling Problem Based on Hybrid Chemical Reaction Optimization Algorithm

Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode.The distributed flexible job-shop scheduling problem(DFJSP)has become a research hot topic in the field of scheduling because its production is closer to reality.The research of DFJSP is of great significance to the organization and management of actual production process.To solve the heterogeneous DFJSP with minimal completion time,a hybrid chemical reaction optimization(HCRO)algorithm is proposed in this paper.Firstly,a novel encoding-decoding method for flexible manufacturing unit(FMU)is designed.Secondly,half of initial populations are generated by scheduling rule.Combined with the new solution acceptance method of simulated annealing(SA)algorithm,an improved method of critical-FMU is designed to improve the global and local search ability of the algorithm.Finally,the elitist selection strategy and the orthogonal experimental method are introduced to the algorithm to improve the convergence speed and optimize the algorithm parameters.In the experimental part,the effectiveness of the simulated annealing algorithm and the critical-FMU refinement methods is firstly verified.Secondly,in the comparison with other existing algorithms,the proposed optimal scheduling algorithm is not only effective in homogeneous FMUs examples,but also superior to existing algorithms in heterogeneous FMUs arithmetic cases.

Optimization of reaction conditions for the electroleaching of manganese from low-grade pyrolusite

In the present study, a response surface methodology was used to optimize the electroleaching of Mn from low-grade pyrolusite. Ferrous sulfate heptahydrate was used in this reaction as a reducing agent in sulfuric acid solutions. The effect of six process variables, including the mass ratio of ferrous sulfate heptahydrate to pyrolusite, mass ratio of sulfuric acid to pyrolusite, liquid-to-solid ratio, current density, leaching temperature, and leaching time, as well as their binary interactions, were modeled. The results revealed that the order of these factors with respect to their effects on the leaching efficiency were mass ratio of ferrous sulfate heptahydrate to pyrolusite 〉 leaching time 〉 mass ratio of sulfuric acid to pyrolusite 〉 liquid-to-solid ratio 〉 leaching temperature 〉 current density. The optimum conditions were as follows： 1.10：1 mass ratio of ferrous sulfate heptahydrate to pyrolusite, 0.9：1 mass ratio of sulfuric acid to pyrolusite, liquid-to-solid ratio of 0.7：1, current density of 947 A/m^2, leaching time of 180 min, and leaching temperature of 73°C. Under these conditions, the predicted leaching efficiency for Mn was 94.1%; the obtained experimental result was 95.7%, which confirmed the validity of the model.

Optimization of SSR-PCR Reaction System and Screening of Polymorphic Primers for RIL Parents

[Objective] This study aimed to establish a high-efficiency and stable SSR amplification system and screen polymorphic primers in order to further compare the tri-group probability analysis method and traditional QTL mapping method. [Method] In this study, we preliminarily screened the 605 pairs of primers evenly distributed on the 12 chromosomes through investigating their polymorphism performance in amplification, and established an optimized SSR-PCR reaction system. [Result] A 10μL SSR-PCR reaction system suitable for rice was set up as fol ows： 2 μl of 10 × Buffer, 2.0 mmol/L Mg2＋ （final concentration）, 0.5 mmol/L dNTPs, 1.0 μmol/L primers （final concentration）, 1 μl of DNA template, 0.15 U Taq DNA polymerase. Among the SSR primers distributed over the genome, 142 pairs that were polymorphic upon the parents were screened. [Conclusion] This study lays a good foundation for sub-sequent QTL mapping studies.

Simultaneous optimization of transit network and public bicycle station network

The traditional manner to design public transportation system is to sequentially design the transit network and public bicycle network. A new public transportation system design problem that simultaneously considers both bus network design and public bicycle network design is proposed. The chemical reaction optimization(CRO) is designed to solve the problem. A shortcoming of CRO is that, when the two-molecule collisions take place, the molecules are randomly picked from the container.Hence, we improve CRO by employing different mating strategies. The computational results confirm the benefits of the mating strategies. Numerical experiments are conducted on the Sioux-Falls network. A comparison with the traditional sequential modeling framework indicates that the proposed approach has a better performance and is more robust. The practical applicability of the approach is proved by employing a real size network.

Integration strategies of hydrogen network in a refinery based on operational optimization of hydrotreating units

Inferior crude oil and fuel oil upgrading lead to escalating increase of hydrogen consumption in refineries.It is imperative to reduce the hydrogen consumption for energy-saving operations of refineries.An integration strategy of hydrogen network and an operational optimization model of hydrotreating(HDT)units are proposed based on the characteristics of reaction kinetics of HDT units.By solving the proposed model,the operating conditions of HDT units are optimized,and the parameters of hydrogen sinks are determined by coupling hydrodesulfurization(HDS),hydrodenitrification(HDN)and aromatic hydrogenation(HDA)kinetics.An example case of a refinery with annual processing capacity of eight million tons is adopted to demonstrate the feasibility of the proposed optimization strategies and the model.Results show that HDS,HDN and HDA reactions are the major source of hydrogen consumption in the refinery.The total hydrogen consumption can be reduced by 18.9%by applying conventional hydrogen network optimization model.When the hydrogen network is optimized after the operational optimization of HDT units is performed,the hydrogen consumption is reduced by28.2%.When the benefit of the fuel gas recovery is further considered,the total annual cost of hydrogen network can be reduced by 3.21×10~7CNY·a^(-1),decreased by 11.9%.Therefore,the operational optimization of the HDT units in refineries should be imposed to determine the parameters of hydrogen sinks base on the characteristics of reaction kinetics of the hydrogenation processes before the optimization of the hydrogen network is performed through the source-sink matching methods.

Green and efficient epoxidation of methyl oleate over hierarchical TS-1

The epoxidation of methyl oleate(MO)was conducted in the presence of aqueous H2O2 as the oxidant and hierarchical TS-1(HTS-1)as the catalyst;the catalyst was synthesized using polyquaternium-6 as the mesopore template.The effects of various parameters,i.e.,H2O2/C=C molar ratio,oxidant concentration,amount of the catalyst,reaction temperature,and time,were systematically studied.Furthermore,response surface methodology(RSM)was used to optimize the conditions to maximize the yield of epoxy MO and to evaluate the significance and interplay of the factors affecting the epoxy MO production.The H2O2/C=C molar ratio and catalyst amount were the determining factors for MO epoxidation,wherein the maximum yield of epoxy MO reached 94.9%over HTS-1 under the optimal conditions.

A Hybrid Algorithm Based on Binary Chemical Reaction Optimization and Tabu Search for Feature Selection of High-Dimensional Biomedical Data

In recent years, there have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. The biomedical data can be analyzed to enhance assessment of at-risk patients and improve disease diagnosis, treatment, and prevention. However, these datasets usually have many features, which contain many irrelevant or redundant information. Feature selection is a solution that involves finding the optimal subset, which is known to be an NP problem because of the large search space. Considering this, a new feature selection approach based on Binary Chemical Reaction Optimization algorithm （BCRO） and k-Nearest Neighbors （KNN） classifier is presented in this paper. Tabu search is integrated with CRO framework to enhance local search capacity. KNN is adopted to evaluate the quality of selected candidate subset. The results for an experiment conducted on nine standard medical datasets demonstrate that the proposed approach outperforms other state-of-the-art methods.

Construction of CuO-modified zeolitic imidazolate framework-9 for photocatalytic hydrogen evolution

An efficient CuO‐modified zeolitic imidazolate framework‐9(ZIF‐9)photocatalyst is successfully prepared at room temperature under mild conditions.It is observed that the ZIF‐9/CuO photocatalyst is effective for H2generation under visible light with sacrificial agent conditions.When the CuO is introduced,the photocatalytic properties of ZIF‐9are greatly improved and when the content of CuO is40%,the photocatalytic activity reaches a maximum of78.74μmol after5h.This results from the200–300nm cube structure of ZIF‐9being able to adsorb more dye molecules and the CuO,which connects with ZIF‐9,greatly improving the electronic transmission efficiency.Moreover,the interaction between the dye molecule Eosin Y(EY)and the catalyst is also studied by transient fluorescence spectroscopy.A series of characterizations,such as SEM,TEM,XPS,XRD,UV‐vis,FTIR,transient fluorescence and photocurrent,are conducted,and the results are in good agreement with the experimental result.In addition,the possible reaction mechanism over EY‐sensitized ZIF‐9/CuO under visible light irradiation is proposed.

High-efficient solar-driven hydrogen production by full-spectrum synergistic photo-thermo-catalytic methanol steam reforming with in-situ photoreduced Pt-CuO_(x) catalyst

Synergy between the intrinsic photon and thermal effects from full-spectrum sunlight for H_(2) production is considered to be central to further improve solar-driven H_(2) production.To that end,the photo-thermocatalyst that demonstrates both photoelectronic and photothermal conversion capabilities have drawn much attention recently.Here,we propose a novel synergistic full-spectrum photo-thermo-catalysis technique for high-efficient H_(2) production by solar-driven methanol steam reforming(MSR),along with the Pt-Cu Oxphoto-thermo-catalyst featuring Pt-Cu/Cu_(2)O/CuO heterojunctions by Pt-mediated in-situ photoreduction of Cu O.The results show that the H_(2) production performance rises superlinearly with increasing light intensity.The optimal H_(2) production rate of 1.6 mol g^(-1) h^(-1) with the corresponding solar-to-hydrogen conversion efficiency of 7%and the CO selectivity of 5%is achieved under 15×sun full-spectrum irradiance(1×sun=1 k W m^(-2))at 180°C,which is much more efficient than the previously-reported Cu-based thermo-catalysts for MSR normally operating at 250~350°C.These attractive performances result from the optimized reaction kinetics in terms of intensified intermediate adsorption and accelerated carrier transfer by long-wave photothermal effect,and reduced activation barrier by short-wave photoelectronic effect,due to the broadened full-spectrum absorbability of catalyst.This work has brought us into the innovative technology of full-spectrum synergistic photothermo-catalysis,which is envisioned to expand the application fields of high-efficient solar fuel production.

Experimental and mechanistic study on chemical looping combustion of caking coal

Under high-temperature batch fluidized bed conditions and by employing juye coal as the raw material,the present study determined the effects of the bed material,temperature,OC/C ratio,steam flow and oxygen carrier cycle on the chemical looping combustion of coal.In addition,the variations taking place in the surface functional groups of coal under different reaction times were investigated,and the variations achieved by the gas released under the pyrolysis and combustion of Juye coal were analyzed.As revealed from the results,the carbon conversion ratio and rate were elevated significantly,and the volume fraction of the outlet CO_(2)remained more than 92%under the oxygen carriers.The optimized reaction conditions to achieve the chemical looping combustion of Juye coal consisted of a temperature of 900℃,an OC/C ratio of 2,as well as a steam flow rate of 0.5 g·min^(-1).When the coal was undergoing the chemical looping combustion,volatiles primarily originated from the pyrolysis of aliphatic-CH_(3)and-CH_(2),and CO and H_(2)were largely generated from the gasification of aromatic carbon.In the CLC process,H_(2)O and CO_(2)began to separate out at 270℃,CH4 and tar began to precipitate at 370℃,and the amount of CO_(2)was continuously elevated with the rise of the temperature.

Controlled Synthesis of Polymers

Controlled synthesis is central to obtaining polymers with accurate structures and excellent performances.Recent research in the controlled synthesis of polymers has focused on optimizing monomers,initiation systems,and reaction conditions.The satisfactory sequence,topological structure,and dispersity have been achieved to satisfy the growing demand for functional polymers.This re-view summarizes the selection of monomers of various types and structures,the innovation of initiation systems,and the optimiza-tion of reaction conditions in the controlled synthesis of polymers and discusses their challenges and opportunities.

Multi-enzyme cascade for sustainable synthesis of L-threo-phenylserine by modulating aldehydes inhibition and kinetic/thermodynamic controls

l-Threonine transaldolase could catalyze the transaldolation of l-threonine and aldehyde to generateβ-hydroxy-α-amino acids with high diastereoselectivity.A novel l-threonine transaldolase(PmLTTA)was identified from Pseudomonas sp.through genome mining.PmLTTA exhibited high activity in the synthesis of l-threo-phenylserine from l-threonine and benzaldehyde,with specific activity of 5.48 U mg-1.However,the application of PmLTTA was impeded by the low conversion ratio and variable diastereoselectivity,which were caused by the toxicity of aldehydes and kinetic/thermodynamic controls in the transaldolation reaction.To solve these issues,alcohol dehydrogenase was used to remove the by-product acetaldehyde,and then carboxylic acid reductase was introduced to alleviate the inhibition of benzaldehyde and toxicity of DMSO.Finally,a multi-enzyme cascade reaction,comprising of PmLTTA,carboxylic acid reductase,alcohol dehydrogenase and glucose dehydrogenase,was constructed to prepare l-threo-phenylserine from cheap benzoic acid,in which alleviated inhibition of aldehydes and desirable diastereoselectivity were achieved.Under the optimized conditions,the conversion ratio of 57.1%and de value of 95.3%were reached.This study provides an efficient and green approach for the synthesis of chiral l-threo-phenylserine from industrial byproduct,and provides guidance for the development of cascade reactions influenced by the toxic intermediates and complicated kinetic/thermodynamic controls.