Evaluation of optimization techniques for an extractive alcoholic fermentation process

The mathematical optimization of a continuous alcoholic fermentation process combined with a flash column under vacuum was studied. The objective was to maximize % yield and productivity in the fermentor. The results using surface response analysis combined with modeling and simulation were compared withy those obtained when the problem was written as a nonlinear programming problem and was solved with a successive quadratic programming (SQP) technique. Two process models were evaluated when the process was optimized using the SQP technique. The first one is a deterministic model, whose kinetic parameters were experimentally determined as functions of the temperature, and the second is a statistical model obtained using the factorial design technique combined with simulation. Although the best result was the one obtained using the rigorous model, the values for productivity and % yield obtained using the simplified model are acceptable, and these models can be used when the development of a rigorous model is excessively difficult, slow, or expensive.     

Annealing contour Monte Carlo algorithm for structure optimization in an off-lattice protein model

We present a space annealing version for a contour Monte Carlo algorithm and show that it can be applied successfully to finding the ground states for an off-lattice protein model. The comparison shows that the algorithm has made a significant improvement over the pruned-enriched-Rosenbluth method and the Metropolis Monte Carlo method in finding the ground states for AB models. For all sequences, the algorithm has renewed the putative ground energy values in the two-dimensional AB model and set the putative ground energy values in the three-dimensional AB model.     

FWAVina: A novel optimization algorithm for protein-ligand docking based on the fireworks algorithm

Protein-ligand docking is an essential process that has accelerated drug discovery. How to accurately and effectively optimize the predominant position and orientation of ligands in the binding pocket of a target protein is a major challenge. This paper proposed a novel ligand binding pose search method called FWAVina based on the fireworks algorithm, which combined the fireworks algorithm with the efficient Broyden-Fletcher-Goldfarb-Shannon local search method adopted in AutoDock Vina to address the pose search problem in docking. The FWA was used as a global optimizer to rapidly search promising poses, and the Broyden-Fletcher-Goldfarb-Shannon method was incorporated into FWAVina to perform an exact local search. FWAVina was developed and tested on the PDBbind and DUD-E datasets. The docking performance of FWAVina was compared with the original Vina program. The results showed that FWAVina achieves a remarkable execution time reduction of more than 50 % than Vina without compromising the prediction accuracies in the docking and virtual screening experiments. In addition, the increase in the number of ligand rotatable bonds has almost no effect on the efficiency of FWAVina. The higher accuracy, faster convergence and improved stability make the FWAVina method a better choice of docking tool for computer-aided drug design. The source code is available at https://github.com/eddyblue/FWAVina/.     

Modelling and optimization of the cyclic regime of an ion-exchange process for sugar juice softening

The cyclic operation of a sugar decalcification system by ion exchange is studied. Although the saturation step can be considered as a pure ion-exchang     

Parameter optimization and experiment verification for a beta radioluminescence nuclear battery

In a beta radioluminescence nuclear battery, the beta energy is converted to light with the phosphor material, and then to electricity via photovoltaic cells. A method to optimize the thickness of phosphor layer is established in this study; the match between the luminescence spectrum and the photovoltaic cell is analyzed. The optimal parameters and output performance of the nuclear battery based on a sandwich-structure ¹⁴⁷Pm/ZnS:Cu/photovoltaic cell are determined with the MCNP, transport theory of light, and detailed balance limit of efficiency. The battery prototypes are fabricated and tested, and the experimental optimal thickness matches that of the theoretical result well.     

Gradient gravitational search: An efficient metaheuristic algorithm for global optimization

The adaptation of novel techniques developed in the field of computational chemistry to solve the concerned problems for large and flexible molecules is taking the center stage with regard to efficient algorithm, computational cost and accuracy. In this article, the gradient‐based gravitational search (GGS) algorithm, using analytical gradients for a fast minimization to the next local minimum has been reported. Its efficiency as metaheuristic approach has also been compared with Gradient Tabu Search and others like: Gravitational Search, Cuckoo Search, and Back Tracking Search algorithms for global optimization. Moreover, the GGS approach has also been applied to computational chemistry problems for finding the minimal value potential energy of two‐dimensional and three‐dimensional off‐lattice protein models. The simulation results reveal the relative stability and physical accuracy of protein models with efficient computational cost. © 2015 Wiley Periodicals, Inc.     

A fast annealing evolutionary algorithm for global optimization

By combining the aspect of population in genetic algorithms (GAs) and the simulated annealing algorithm (SAA), a novel algorithm, called fast annealing evolutionary algorithm (FAEA), is proposed. The algorithm is similar to the annealing evolutionary algorithm (AEA), and a very fast annealing technique is adopted for the annealing procedure. By an application of the algorithm to the optimization of test functions and a comparison of the algorithm with other stochastic optimization methods, it is shown that the algorithm is a highly efficient optimization method. It was also applied in optimization of Lennard-Jones clusters and compared with other methods in this study. The results indicate that the algorithm is a good tool for the energy minimization problem.     

Design of experiment techniques for the optimization of chromatographic analysis conditions: A review

Design of experiment (DoE) techniques have been widely used in the field of chromatographic parameters optimization as a valuable tool. A systematic literature review of the available DoE techniques applied to the development of a chromatographic analysis method is presented in this paper. First, the most common available designs and the implementation steps of DoE are comprehensively introduced. Then the studies in recent 10 years for the application of DoE techniques in various chromatographic techniques are discussed, such as capillary electrophoresis, liquid chromatography, gas chromatography, thin-layer chromatography, and high-speed countercurrent chromatography. Current problems and future outlooks are finally given to provide a certain inspiration of research in the application of DoE techniques to the different chromatographic techniques field. This review contributes to a better understanding of the DoE techniques for the efficient optimization of chromatographic analysis conditions, especially for the analysis of complex systems, such as multicomponent drugs and natural products.     

Dramatic performance enhancements for the FASTER optimization algorithm

FASTER is a combinatorial optimization algorithm useful for finding low-energy side-chain configurations in side-chain placement and protein design calculations. We present two simple enhancements to FASTER that together improve the computational efficiency of these calculations by as much as two orders of magnitude with no loss of accuracy. Our results highlight the importance of choosing appropriate initial configurations, and show that efficiency can be improved by stringently limiting the number of positions that are allowed to relax in response to a perturbation. The changes we describe improve the quality of solutions found for large-scale designs, and allow them to be found in hours rather than days. The improved FASTER algorithm finds low-energy solutions more efficiently than common optimization schemes based on the dead-end elimination theorem and Monte Carlo. These advances have prompted investigations into new methods for force field parameterization and multiple state design.     

An algorithm for geometry optimization without analytical gradients

A numerical algorithm for locating both minima and transition states designed for use in the ab initio program package GAUSSIAN 82 is presented. It is based on the RFO method of Simons and coworkers and is effectively the numerical version of an analytical algorithm (OPT = EF) previously published in this journal. The algorithm is designed to make maximum use of external second derivative information obtained from prior optimizations at lower levels of theory. It can be used with any wave function for which an energy can be calculated and is about two to three times faster than the default DFP algorithm (OPT = FP) supplied with GAUSSIAN 82.     

A novel algorithm for spectral interval combination optimization

In this study, a new wavelength interval selection algorithm named as interval combination optimization (ICO) was proposed under the framework of model population analysis (MPA). In this method, the full spectra are divided into a fixed number of equal-width intervals firstly. Then the optimal interval combination is searched iteratively under the guide of MPA in a soft shrinkage manner, among which weighted bootstrap sampling (WBS) is employed as random sampling method. Finally, local search is conducted to optimize the widths of selected intervals. Three NIR datasets were used to validate the performance of ICO algorithm. Results show that ICO can select fewer wavelengths with better prediction performance when compared with other four wavelength selection methods, including VISSA, VISSA-iPLS, iVISSA and GA-iPLS. In addition, the computational intensity of ICO is also economical, benefit from fewer tune parameters and faster convergence speed.     

Quantum algorithm for alchemical optimization in material design

The development of tailored materials for specific applications is an active field of research in chemistry, material science and drug discovery. The number of possible molecules obtainable from a set of atomic species grow exponentially with the size of the system, limiting the efficiency of classical sampling algorithms. On the other hand, quantum computers can provide an efficient solution to the sampling of the chemical compound space for the optimization of a given molecular property. In this work, we propose a quantum algorithm for addressing the material design problem with a favourable scaling. The core of this approach is the representation of the space of candidate structures as a linear superposition of all possible atomic compositions. The corresponding ‘alchemical’ Hamiltonian drives the optimization in both the atomic and electronic spaces leading to the selection of the best fitting molecule, which optimizes a given property of the system, e.g., the interaction with an external potential as in drug design. The quantum advantage resides in the efficient calculation of the electronic structure properties together with the sampling of the exponentially large chemical compound space. We demonstrate both in simulations and with IBM Quantum hardware the efficiency of our scheme and highlight the results in a few test cases. This preliminary study can serve as a basis for the development of further material design quantum algorithms for near-term quantum computers.

‘Alchemical’ quantum algorithm for the simultaneous optimisation of chemical composition and electronic structure for material design. By exploiting quantum mechanical principles this approach will boost drug discovery in the near future.     

Novel hybrid process for the conversion of microcrystalline cellulose to value-added chemicals: part 1: process optimization

In this paper, a novel hybrid process for the treatment of microcrystalline cellulose (MCC) under hot-compressed water was investigated by applying constant direct current on the reaction medium. Constant current range from 1A to 2A was applied through a cylindrical anode made of titanium to the reactor wall. Reactions were conducted using a specially designed batch reactor (450 mL) made of SUS 316 stainless steel for 30–120 min of reaction time at temperature range of 170–230 °C. As a proton donor H₂SO₄ was used at concentrations of 1–50 mM. Main hydrolysis products of MCC degradation in HCW were detected as glucose, fructose, levulinic acid, 5-HMF, and furfural. For the quantification of these products, High Performance Liquid Chromatography (HPLC) and Gas Chromatography with Mass Spectroscopy (GC–MS) were used. A ½ fractional factorial design with 2-level of four factors; reaction time, temperature, H₂SO₄ concentration and applied current with 3 center points were built and responses were statistically analyzed. Response surface methodology was used for process optimization and it was found that introduction of 1A current at 200 °C to the reaction medium increased Total Organic Carbon (TOC) and cellulose conversions to 62 and 81 %, respectively. Moreover, application of current diminished the necessary reaction temperature and time to obtain high TOC and cellulose conversion values and hence decreased the energy required for cellulose hydrolysis to value added chemicals. Applied current had diverse effect on levulinic acid concentration (29.9 %) in the liquid product (230 °C, 120 min., 2 A, 50 mM H₂SO₄).     

Statistical experimental design for bioprocess modeling and optimization analysis: repeated-measures method for dynamic biotechnology process

The statistical design of experiments (DOE) is a collection of predetermined settings of the process variables of interest, which provides an efficient procedure for planning experiments. Experiments on biological processes typically produce long sequences of successive observations on each experimental unit (plant, animal, bioreactor, fermenter, or flask) in response to several treatments (combination of factors). Cell culture and other biotech-related experiments used to be performed by repeated-measures method of experimental design coupled with different levels of several process factors to investigate dynamic biological process. Data collected from this design can be analyzed by several kinds of general linear model (GLM) statistical methods such as multivariate analysis of variance (MANOVA), univariate ANOVA (time split-plot analysis with randomization restriction), and analysis of orthogonal polynomial contrasts of repeated factor (linear coefficient analysis). Last, regression model was introduced to describe responses over time to the different treatments along with model residual analysis. Statistical analysis of biprocess with repeated measurements can help investigate environmental factors and effects affecting physiological and bioprocesses in analyzing and optimizing biotechnology production.     

An adaptive immune optimization algorithm for energy minimization problems

Based on the immune theory of biology, a novel evolutionary algorithm, adaptive immune optimization algorithm (AIOA), is proposed. In AIOA, density regulation and immune selection is adopted to control the individual diversity and the convergence adaptively. By an application of the algorithm to the optimization of test functions, it is shown that the algorithm is a highly efficient optimization method compared with other stochastic optimization methods. The algorithm was also applied to the optimization of Lennard-Jones clusters, and the results show that the method can find the optimal structure of N相似文献   

A novel conformation optimization model and algorithm for structure-based drug design

In this paper, we present a multi-scale optimization model and an entropy-based genetic algorithm for molecular docking. In this model, we introduce to the refined docking design a concept of residue groups based on induced-fit and adopt a combination of conformations in different scales. A new iteration scheme, in conjunction with multi-population evolution strategy, entropy-based searching technique with narrowing down space and the quasi-exact penalty function, is developed to address the optimization problem for molecular docking. A new docking program that accounts for protein flexibility has also been developed. The docking results indicate that the method can be efficiently employed in structure-based drug design.     

Selection of solvent for the mechanical activation process: A molecular dynamics simulation and experiment study

Herein, molecular dynamics simulations and experiments were carried to select solvent for the mechanical activation process. The interaction between solvent and each component of the Al‐PTFE mechanical activated energy composites has been studied by means of molecular dynamics. Then, the status of Al and PTFE in solvent was analyzed, and the microstructure of the composite was also studied combined with experiment. At last, the mechanical activated energy composites were prepared with n‐hexane as the solvent. The results show that the adsorption of PTFE, toluene, and hexane on the (0 0 1), (0 1 0), and (100) surface of Al₂O₃ is stable. Al₂O₃ (0 0 1) surface and Al₂O₃ (0 1 0) surface interact with 3 substances mainly via the electrostatic force. Al₂O₃ (1 0 0) surface interacts with 3 substances mainly via the van der Waals force. The binding energies of toluene‐Al₂O₃ and hexane‐Al₂O₃ are larger than PTFE‐Al₂O₃. PTFE cannot adsorb on the surface of aluminum in the existence of n‐hexane and toluene. n‐Hexane can make PTFE disperse uniformly, and a considerable part of these PTFE will be coated on the surface of Al after n‐hexane is removed. The uniformity of Al/PTFE mechanical activated energetic composites prepared in n‐hexane is good, and it increases with the milling time.     

Nanostructured MnO2: an efficient and robust water oxidation catalyst

Nanostructured MnO(2) exhibits a high turnover frequency for oxygen evolution under visible light and high stability in strong acidic conditions.     

Mixing parameters for geometry optimization using the Hamiltonian algorithm

We study the mixing parameters for the search of an optimal geometry using the Hamiltonian algorithm (HA) combined with ab initio molecular orbital calculations. We choose the C?CC?CC?CC dihedral angle of the butane molecule as an example. HF/3-21G level calculations are employed as the molecular orbital calculations. The distributions of the eigenvalues of mixing coefficients are fitted with the linear, quadratic, and quartic functions. Analyses of HA calculations both up to 2,000 and 60,000 iterative calculations show a possibility that the mixing process reduces the number of iterations. The low energy HF/3-21G, B3LYP/6-31G**, and PCM B3LYP/aug-cc-pVDZ optimized structures of the N-acetyl l-histidine N??-methyl amide and four water molecule supermolecule were also determined using the HA optimization method and compared to the recently determined thought to be global minimum energy structure.