Data-driven modeling based on kernel extreme learning machine for sugarcane juice clarification

Multi-Objective Optimization of Sugarcane Milling System Operations Based on a Deep Data-Driven Model

The extraction of sugarcane juice is the first step of sugar production. The optimal values of process indicators and the set values of operating parameters in this process are still determined by workers' experience, preventing adaptive adjustment of the production process. To address this issue, a multi-objective optimization framework based on a deep data-driven model is proposed to optimize the operation of sugarcane milling systems. First, the sugarcane milling process is abstracted as the interaction of material flow, energy flow, and information flow (MF-EF-IF) by introducing synergetic theory, and each flow's order parameters and state parameters are obtained. Subsequently, the state parameters of the subsystems are taken as inputs, and the order parameters-including the grinding capacity, electric consumption per ton of sugarcane, and sucrose extraction-are produced as outputs. A collaborative optimization model of the MF-EF-IF of the milling system is established by using a deep kernel extreme learning machine (DK-ELM). The established milling system model is applied for an improved multi-objective chicken swarm optimization (IMOCSO) algorithm to obtain the optimal values of the order parameters. Finally, the milling process is described as a Markov decision process (MDP) with the optimal values of the order parameters as the control objectives, and an improved deep deterministic policy gradient (DDPG) algorithm is employed to achieve the adaptive optimization of the operating parameters under different working conditions of the milling system. Computational experiments indicate that enhanced performance is achieved, with an increase of 3.2 t per hour in grinding capacity, a reduction of 660 W per ton in sugarcane electric consumption, and an increase of 0.03% in the sucrose extraction.

Structural, physicochemical and functional properties of Semen Ziziphi Spinosae protein

Semen Ziziphi Spinosae (Ziziphus jujuba Mill. var. spinosa (Bunge) Hu ex H. F. Chou) is a functional food and a traditional Chinese medicine (TCM) in China. Herein, Semen Ziziphi Spinosae protein (SZSP) was prepared by an alkaline extraction and acid precipitation method, of which the structural, physicochemical, functional and emulsion properties were investigated. Results showed that SZSP contained an ideal amino acid composition. The structural properties of the proteins were characterized using Fourier transform infrared spectroscopy (FTIR), relative fluorescence and circular dichroism (CD) spectroscopy analysis. The electrophoresis profiles showed that the main molecular weight of the protein components was about 10-40 kDa and contained some glycoproteins. Differential scanning calorimetry analysis indicated that the denaturation temperature of SZSP was 110.5 °C. The functional properties showed that SZSP has good water and oil absorption capacity, high emulsifying ability and foaming stability. The overall results suggest that SZSP is a promising protein source for the functional food industry.