Advanced Control Synthesis for Reverse Osmosis Water Desalination Processes.

In this study, robust control synthesis has been applied to a reverse osmosis desalination plant whose product water flow and salinity are chosen as two controlled variables. The reverse osmosis process has been selected to study since it typically uses less energy than thermal distillation. The aim of the robust design is to overcome the limitation of classical controllers in dealing with large parametric uncertainties, external disturbances, sensor noises, and unmodeled process dynamics. The analyzed desalination process is modeled as a multi-input multi-output (MIMO) system with varying parameters. The control system is decoupled using a feed forward decoupling method to reduce the interactions between control channels. Both nominal and perturbed reverse osmosis systems have been analyzed using structured singular values for their stabilities and performances. Simulation results show that the system responses meet all the control requirements against various uncertainties. Finally the reduced order controller provides excellent robust performance, with achieving decoupling, disturbance attenuation, and noise rejection. It can help to reduce the membrane cleanings, increase the robustness against uncertainties, and lower the energy consumption for process monitoring.

Optimization and inventory management under stochastic demand using metaheuristic algorithm.

This study considers multi-period inventory systems for optimizing profit and storage space under stochastic demand. A nonlinear programming model based on random demand is proposed to simulate the inventory operation. The effective inventory management system is realized using a multi-objective grey wolf optimization (MOGWO) method, reducing storage space while maximizing profit. Numerical outcomes are used to confirm the efficacy of the optimal solutions. The numerical analysis and tests for multi-objective inventory optimization are performed in the four practical scenarios. The inventory model's sensitivity analysis is performed to verify the optimal solutions further. Especially the proposed approach allows businesses to optimize profits while regulating the storage space required to operate in inventory management. The supply chain performance can be significantly enhanced using inventory management strategies and inventory management practices. Finally, the novel decision-making strategy can offer new insights into effectively managing digital supply chain networks against market volatility.

Active management strategy for supply chain system using nonlinear control synthesis.

Nonlinear dynamical behaviours with chaotic phenomena are commonly observed in a typical logistics model and supply chain system. Bullwhip effect has been widely recognized as one of the main issues on affecting the supply chain management. In essence, this phenomenon will lead to unnecessary consumption and waste of natural and social resources by demand variability amplification as moving up in the supply chain networks. However, traditional modelling approaches may become complicated in dealing with uncertainty and chaotic behaviour that are prevalent in real supply chains. System dynamics theory has been employed as a potentially effective strategy to cope with chaotic supply chains which are unpredictable behaviours in time. Four-dimensional differential equations which exhibit chaotic behaviours are constructed to describe a multi-echelon supply chain with bullwhip effect. Furthermore, modern control theory is applied to deal with the multi-stage supply chain optimization problems against disruptions. Specifically, the novel fractional order adaptive sliding mode control (FO-ASMC) algorithm has been implemented for ensuring efficient supply chain management. In addition, the chaos synchronization scheme is implemented in an attempt to regulate the supply chain systems under the impact of extensive uncertainties caused by tumultuous real market. It is found that the chaos synchronization is effectively realised by new FO-ASMC theory to manage advanced supply chain networks. Finally, this advanced management optimization offers a new class of intelligent applications that connects demand to supply and planning to execution across the entire supply chains.

Seaport throughput forecasting and post COVID-19 recovery policy by using effective decision-making strategy: A case study of Vietnam ports.

This study deals with the dynamic interactions between seaports and decision-making strategy for seaport operations by utilizing four-dimensional fractional Lotka-Volterra competition model under frequently disrupted by time-delay factor. Nonlinear analysis methods, including equilibrium analysis, stability evaluation, and time series investigation, are intensely explored to describe the cooperation and competition dynamics in maritime logistics. The dynamical analysis indicates that the port competition system shows a complex and highly nonlinear behaviour, notably illustrating unstable equilibria and even chaotic phenomena. Besides, nonlinear dynamical interactions in seaport management have been analysed by exploiting fractional calculus (FC) and system dynamics theory. Novel multi-criteria decision-making strategies realized by the neural network prediction controller (NNC) and adaptive fractional-order super-twisting sliding mode control (AFOSTSM) have been presented for dealing with throughput dynamics under parametric perturbations and external disturbances. Particularly, the active control algorithms are implemented to ensure the recovery strategy for throughput growth of Vietnam ports in the post-coronavirus (COVID-19) pandemic era. The case study has confirmed the efficacy of the proposed strategy by using system dynamics and control theory. The simulation results show that the average growth rates of container throughput can be ensured up to 7.46% by exploiting resilience management scheme. The presented method can be also utilized for providing managerial insights and solutions on efficient port operations. In addition, the control strategies with neural network forecasting can help managers obtain timely and cost-effective decision-making policy for port sustainability against unprecedented impacts on global supply chains related to COVID-19 pandemic.