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Optimal control of batch crystallization systems is still a focus and hot topic in the field of industrial crystallization, which seriously affects the consistency of batch product quality. In this paper, a new method with a new objective function and improved optimization algorithm was proposed for optimization of crystal size distribution (CSD) in case of fine crystals occurrence. The new objective function was developed with better margin metric and weighting technique to minimize fine crystal mass, meanwhile, a newly constructed sinusoidal weight function was introduced to improve the particle swarm optimization (PSO) algorithm. A precise control of CSD with suppressed numerical discrepancy caused by fine crystals removal was developed by combining seed recipe and temperature-swing. In addition, the effects of temperature curve segments on CSD during process optimization were systematically investigated to achieve optimal results. Two typical batch cooling crystallization systems were used to verify the effectiveness of the proposed method in controlling product CSD while minimizing fine crystal mass. Results demonstrated that the desired product CSD can be achieved with minor errors while the fine crystals could be shrunk to be negligible, i.e., the fine crystal mass and number can be reduced by over 90%. This work has an important guiding significance for the removal of fine crystals in industrial crystallization processes, especially when only operational optimization rather than equipment updating is considered.

Reducing production costs and energy consumption has always been the focus of industrial production today, which is driving the chemical industries from this old model towards developing sustainable processes with high efficiency and environmental protection. In this paper, three energy-saving and environment-friendly extractive distillation processes were designed and optimized to separate the methanol/water/toluene mixture. The extractive distillation process was improved by preheating the feed stream using a solvent circulation stream, the thermal energy utilization efficiency has been enhanced. Meanwhile, the process parameters were optimized by multi-objective genetic algorithm (MOGA) taking total annual cost (TAC), CO2emission (ECO2), and separation efficiency of extractive section (Eext) as objective functions. Compared with the existing process, the best separation process can reduce the TAC by up to 20.0 % and the ECO2by up to 30.0 %. In addition, the validity and rationality of the separation processes were verified by analyzing the influence of sensitive variables and exergy losses. This study provides a reference for the separation and energy saving of wastewater containing methanol and toluene.

Seed recipe is known to directly impact crystal growth behavior and product performance in crystallization processes. However, effectively evaluating and reducing the uncertain influence of seed recipe on product consistency in batch crystallization is challenging. This paper utilizes uncertainty analyses based on the population balance model to evaluate the impact of seed recipe destabilization. Monte Carlo simulation (MCS) was employed as a direct and efficient method to quantify the risk of failing to achieve the desired product crystal size distribution (CSD). Sensitivity analysis was conducted by integrating the modified Morris method with the PAWN method to analyze the effects of individual parameters and all parameters in the seed recipe from local and global perspectives. To achieve the targeted product CSD in the presence of seed recipe uncertainties, a novel linear weighted multi-objective function was developed to establish the optimal cooling profile, which was optimized by the enhanced particle swarm optimization (PSO) algorithm. The results demonstrated that even under seed recipe uncertainties, the targeted product CSD can be achieved with minimal error through cooling profile optimization alone, without dissolution.

In this paper, a novel hybrid variable selection method for model building by near-infrared (NIR) spectroscopy is proposed for composition measurement in industrial processes. A double-layer structure is designed for variable selection by combining Fisher's linear discriminant combined population analysis (FCPA) and an improved binary cuckoo search algorithm (IBCS). The Fisher classifier combined with model population analysis is used to select the variable interval wherein the useful variables are roughly located even when strong multicollinearity exists among spectral variables. Opposition-based learning (OBL) and jumping genes (JG) are introduced to improve the binary cuckoo search algorithm for the fine selection of key variables, thus avoiding the loss of excellent solutions due to randomness and the local optimum. Different variable selection methods were used to select variables for beer, corn, and diesel fuel datasets, and the partial least squares (PLS) algorithms were used to build calibration models to predict the original extract concentration of beer, the protein and starch content of corn, and the boiling point of diesel fuel, respectively. The results showed that the proposed PLS modeling method based on FCPA-IBCS has higher fitting accuracy and smaller prediction errors.

In this paper, a seed recipe design is proposed for batch cooling crystallization to obtain the desired product attributes including product yield and product size distribution, based on simulation studies and experiments on β-l-glutamic acid (β-LGA) crystallization. The impact of seed recipe on product attributes is investigated based on the population balance model (PBM) simulations with respect to the size-dependent growth of crystals. It is found that the product yield is primarily affected by the seed loading ratio (SLR) and the batch time, but less affected by the mean size and variance of seeds. Smaller seeds could improve the product yield, and in contrast, larger seeds facilitate the growth into larger crystals but require a larger SLR to ensure the product yield. By introducing an objective function for optimization with the above PBM, a seed recipe design is given for obtaining the desired product attributes as above-mentioned. In addition, it is found that washing seeds by the solvent is necessary to ensure seed quality for quantitative seed recipe design and implementation, by comparing three different seed preparation methods. Simulation tests and experiments well demonstrate the effectiveness of the proposed seed recipe design for seeded batch cooling crystallization.

Nucleation as a unique phenomenon in the crystallization process has always been a hot topic. With the development of nucleation theory and process analysis technology (PAT), nucleation control technology (NCT) has been widely applied in industrial crystallization processes. This paper provides a comprehensive review of advancements in nucleation measurement and control technologies within crystallization processes, systematically analyzing the principles, advantages, disadvantages, and application scopes of various NCTs, and particularly the factors affecting nucleation and nucleation-assisted technologies, including ultrasound, gassing, and membranes, are elaborated. These technologies are crucial for managing the unpredictable nature of nucleation to improve the consistency and quality of crystalline products. These advanced or novel control technologies are very attractive in providing continuous and stable high-purity seeds without externally adding additional seeds and have proven to be effective alternative methods for generating internal seeds. In addition, the challenges and opportunities were pointed out from the stability, controllability, and consistency of nucleation to promoting the efficient development of crystallization technology. NCTs play a vital role in industrial applications by ensuring control over the size, purity, and morphology of crystals. This review also explains the application of NCTs in industrial scenarios, positioning these technologies as essential for the advancement of various industrial applications.

Population balance equations (PBEs) in conjunction with appropriate kinetic expressions have been extensively employed as modeling tools to elucidate the dynamic behavior of particulate processes such as precipitation, crystallization, aerosolization, microbial growth, and cell population growth. It is still challenging to ensure the stability and accuracy of solutions since the PBE for a crystallization system represents a hyperbolic integral-partial differential equation that describes the temporal and spatial evolution of the crystal shape and size distribution. An improved high-resolution (HR) algorithm along with new flux limiters to suppress spurious oscillations and improve accuracy was proposed based on batch crystallization in this paper. Through systematic research and analysis of the influence of the limiter function on the HR algorithm, a new limiter function was designed to improve solution accuracy and stability. Simulation cases considering crystal size-dependent and -independent growth with and without nucleation were used to test the effectiveness of the presented algorithm. Results demonstrated that the new flux limiter presents marked superiority compared with the classical Van Leer flux limiter, and the limiter functions should be reasonably selected under different Courant–Friedrichs–Lewy conditions to improve the accuracy of the HR algorithm. In addition, an ameliorated gradient ratio was further designed to solve the problem of decreased accuracy at discontinuities.

The liquid–liquid equilibrium data of the two ternary systems of n-propyl acetate + n-propanol + 1-butyl-3-methylimidazolium hydrogensulfate and isopropyl acetate + isopropyl alcohol +1-butyl-3-methylimidazolium hydrogensulfate were measured at 298.15 K and 318.15 K and under atmospheric pressure in this work. The extraction effect of ionic liquids was discussed by comparing the values of the distribution coefficient and separation factor. The influence of temperature on the liquid–liquid equilibrium data was discussed. The binary interaction parameters of the nonrandom two-liquid and universal quasichemical models were obtained by regressing the experimental data.

• An improved model for recognizing factory personnel's abnormal behavior in complicated situations has been proposed. • Detection accuracy is improved by introducing a backbone network enhanced by Bi-level routing attention. • The robustness of the model is enhanced and the complexity is reduced by the C3 module of the neck network is replaced with the CNeB module. • The mean average precision of the model was improved to 86.7 % and the detection speed reached the standard of real-time detection. The real-time and accurate recognition of abnormal behavior among factory personnel helps enhance their awareness of hazardous environments, thereby reducing the occurrence of accidents. This paper proposes a behavior recognition network based on an attention mechanism and a high-efficiency convolution module. The Bi-Level Routing Attention was introduced to the backbone network, thus enhancing the attention of the recognition network to the target region effectively. The recognition accuracy was further strengthened by improving the neck network based on the ConvNeXt Block module while reducing the model complexity. Thirteen additional recognition models were constructed to enhance the original network from various perspectives. Subsequently, the mean average precision and detection speed of each model were evaluated. Experimental results demonstrated that the detection accuracy of the target recognition network proposed in this paper has been significantly improved, the detection speed meets the real-time requirements, and the comprehensive performance is the most superior compared with other diverse and improved networks. The proposed recognition model can accurately identify a variety of factory personnel's abnormal behaviors in real-time, and it has practical application significance for the problem of personnel safety identification in the factory. [ABSTRACT FROM AUTHOR]

Seeding is a significant but challenging task for optimizing crystallization process operation, product quality, and process efficiency. With the development of modern crystallization technology, there have been considerable advancements in seeding techniques in recent years. This article elaborates on how seeds affect the crystallization process, clarifies the qualitative connection between seeding parameters, defines the variables and product quality attributes based on a variable-based causal graph, and analyzes various possible factors that result in seeding-technique failure based on an Ishikawa diagram. The development of seeding-technique studies and existing problems are then systematically reviewed and discussed based on the qualitative and quantitative characteristics of seeds and the factors that influence seeding efficiency. Based on extensive research accomplishments, the challenges and opportunities for utilizing seeding techniques are analyzed, and potentially valuable seeding-technique topics and directions for improving crystallization are presented. In addition, a framework to guide seed recipe design and optimization is defined by combining simulation and experimental verification.