Contract Mechanism of Water Environment Regulation for Small and Medium Sized Enterprises Based on Optimal Control Theory

The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of treatment. How enterprises can make reasonable decisions on their water environment behavior based on the external environment and their own factors is of great significance for scientifically and effectively designing water environment regulation mechanisms. Based on optimal control theory, this study investigates the design of contractual mechanisms for water environmental regulation for small and medium-sized enterprises. The enterprise is regarded as an independent economic entity that can adopt optimal control strategies to maximize its own interests. Based on the participation of multiple subjects including the government, enterprises, and the public, an optimal control strategy model for enterprises under contractual water environmental regulation is constructed using optimal control theory, and a method for calculating the amount of unit pollutant penalties is derived. The water pollutant treatment cost data of a paper company is selected to conduct empirical numerical analysis on the model. The results show that the increase in the probability of government regulation and public participation, as well as the decrease in local government protection for enterprises, can achieve the same regulatory effect while reducing the number of administrative penalties per unit. Finally, the implementation process of contractual water environmental regulation for small and medium-sized enterprises is designed.

A Research Overview on Arachidonic Acid:Fermentation Regulation and Purification

Arachidonic acid （ARA or AA）, one of the most important polyunsaturated fatty acids （PUFAs）, has various physiological activities and positive effects on human health. ARA production by Mortierella alpina has become a hot topic in recent years, owing to that it is effective, safe and easy to control. How to improve ARA yield and purification efficiency is important to ARA production in M. alpina. Therefore, in this review, we summarized some methods to improve ARA yield： optimization of culture conditions, mycelium aging technologies and metabolic regulation, and the commonly used methods for ARA isolation and purification, to provide a theoretical basis for ARA production by M. alpina fermentation.

An Optimal Control Strategy Combining SVM with RGA for Improving Fermentation Titer

An optimal control strategy is proposed to improve the fermentation titer,which combines the support vector machine(SVM)with real code genetic algorithm(RGA).A prediction model is established with SVM for penicillin fermentation processes,and it is used in RGA for fitting function.A control pattern is proposed to overcome the coupling problem of fermentation parameters,which describes the overall production condition.Experimental results show that the optimal control strategy improves the penicillin titer of the fermentation process by 22.88%,compared with the routine operation.

Design of Environmental Monitoring and Control System for Large-scale Pig House with Fermentation Bed

The design and assembly of environmental monitoring and control system for large-scale pig house with fermentation bed helped to solve the problem of environmental automatic control in piggery.The sensors would monitor the temperature,humidity,light,wind direction,wind speed,CO2,NH3and other parameters.On-line real-time data collection was achieved.The expert system was constructed to control the temperature in piggery below 30℃,to control the air and mattress humidities higher than 65%.Under the conditions of different season or different wind speed,even in day and night,the control actuators were different.The actuators included fanning wet curtain,lighting,micro spraying,spraying,propeller fan,electric aluminum alloy shutter and spraying systems on the roof.The actuators were integrated,and they control the piggery environment simultaneously.The system also designed the remote video monitor interface,parameter-monitoring curved interface and operation interface,which provided a good man-machine interface.

Regulations for the Manufacture and Control of Live Poliovirus Vaccine: International Experience and China’s Path

The eradication of poliomyelitis is a landmark achievement in the history of public health, providing strong protection for children’s health. The introduction of the Chinese Regulations for the Manufacture and Control of Live Poliovirus Vaccine is a prerequisite and safeguard for the large-scale production and use of domestically produced live poliovirus vaccines, serving as an indispensable component of vaccine safety. This article, based on archival documents, letters, collections of essays, and oral interviews, examines the historical experience of the development of Chinese Regulations for the Manufacture and Control of Live Poliovirus Vaccine. It contends that the emphasis on localization and the active engagement in international cooperation are critical factors in the swift introduction of Chinese Regulations for the Manufacture and Control of Live Poliovirus Vaccine.

Controlling the dynamic behavior of decentralized cluster through centralized approaches

How to control the dynamic behavior of large-scale artificial active matter is a critical concern in experimental research on soft matter, particularly regarding the emergence of collective behaviors and the formation of group patterns. Centralized systems excel in precise control over individual behavior within a group, ensuring high accuracy and controllability in task execution. Nevertheless, their sensitivity to group size may limit their adaptability to diverse tasks. In contrast, decentralized systems empower individuals with autonomous decision-making, enhancing adaptability and system robustness. Yet, this flexibility comes at the cost of reduced accuracy and efficiency in task execution. In this work, we present a unique method for regulating the centralized dynamic behavior of self-organizing clusters based on environmental interactions. Within this environment-coupled robot system, each robot possesses similar dynamic characteristics, and their internal programs are entirely identical. However, their behaviors can be guided by the centralized control of the environment, facilitating the accomplishment of diverse cluster tasks. This approach aims to balance the accuracy and flexibility of centralized control with the robustness and task adaptability of decentralized control. The proactive regulation of dynamic behavioral characteristics in active matter groups, demonstrated in this work through environmental interactions, holds the potential to introduce a novel technological approach and provide experimental references for studying the dynamic behavior control of large-scale artificial active matter systems.

Self-Triggered Set Stabilization of Boolean Control Networks and Its Applications

Set stabilization is one of the essential problems in engineering systems, and self-triggered control(STC) can save the storage space for interactive information, and can be successfully applied in networked control systems with limited communication resources. In this study, the set stabilization problem and STC design of Boolean control networks are investigated via the semi-tensor product technique. On the one hand, the largest control invariant subset is calculated in terms of the strongly connected components of the state transition graph, by which a graph-theoretical condition for set stabilization is derived. On the other hand, a characteristic function is exploited to determine the triggering mechanism and feasible controls. Based on this, the minimum-time and minimum-triggering open-loop, state-feedback and output-feedback STCs for set stabilization are designed,respectively. As classic applications of self-triggered set stabilization, self-triggered synchronization, self-triggered output tracking and self-triggered output regulation are discussed as well. Additionally, several practical examples are given to illustrate the effectiveness of theoretical results.

Optimal Cooperative Secondary Control for Islanded DC Microgrids via a Fully Actuated Approach

DC-DC converter-based multi-bus DC microgrids(MGs) in series have received much attention, where the conflict between voltage recovery and current balancing has been a hot topic. The lack of models that accurately portray the electrical characteristics of actual MGs while is controller design-friendly has kept the issue active. To this end, this paper establishes a large-signal model containing the comprehensive dynamical behavior of the DC MGs based on the theory of high-order fully actuated systems, and proposes distributed optimal control based on this. The proposed secondary control method can achieve the two goals of voltage recovery and current sharing for multi-bus DC MGs. Additionally, the simple structure of the proposed approach is similar to one based on droop control, which allows this control technique to be easily implemented in a variety of modern microgrids with different configurations. In contrast to existing studies, the process of controller design in this paper is closely tied to the actual dynamics of the MGs. It is a prominent feature that enables engineers to customize the performance metrics of the system. In addition, the analysis of the stability of the closed-loop DC microgrid system, as well as the optimality and consensus of current sharing are given. Finally, a scaled-down solar and battery-based microgrid prototype with maximum power point tracking controller is developed in the laboratory to experimentally test the efficacy of the proposed control method.

Adaptive Optimal Output Regulation of Interconnected Singularly Perturbed Systems With Application to Power Systems

This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems(SPSs) with unknown dynamics based on reinforcement learning(RL).Taking into account the slow and fast characteristics among system states,the interconnected SPS is decomposed into the slow time-scale dynamics and the fast timescale dynamics through singular perturbation theory.For the fast time-scale dynamics with interconnections,we devise a decentralized optimal control strategy by selecting appropriate weight matrices in the cost function.For the slow time-scale dynamics with unknown system parameters,an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement data.By combining the slow and fast controllers,we establish the composite decentralized adaptive optimal output regulator,and rigorously analyze the stability and optimality of the closed-loop system.The proposed decomposition design not only bypasses the numerical stiffness but also alleviates the high-dimensionality.The efficacy of the proposed methodology is validated by a load-frequency control application of a two-area power system.

Event-triggered robust cooperative output regulation for a class of linear multi-agent systems with an unknown exosystem

This paper investigates the robust cooperative output regulation problem for a class of heterogeneousuncertain linear multi-agent systems with an unknown exosystem via event-triggered control (ETC). By utilizingthe internal model approach and the adaptive control technique, a distributed adaptive internal model isconstructed for each agent. Then, based on this internal model, a fully distributed ETC strategy composed ofa distributed event-triggered adaptive output feedback control law and a distributed dynamic event-triggeringmechanism is proposed, in which each agent updates its control input at its own triggering time instants. It isshown that under the proposed ETC strategy, the robust cooperative output regulation problem can be solvedwithout requiring either the global information associated with the communication topology or the bounds ofthe uncertain or unknown parameters in each agent and the exosystem. A numerical example is provided toillustrate the effectiveness of the proposed control strategy.

Living/controlled polymerization of vinylpyridines into sequence-regulated copolymers by Lewis pair

The poly(vinylpyridine)(PVP) based(co)polymers are of particular interest in materials science, due to their multifunctionality and diverse applications. So far, there is no report on the sequence-regulated copolymerization of vinylpyridines(VPs) and methacrylate monomer in one-step manner yet. Here we designed and synthesized a series of guanidine phosphines as Lewis base(LB), which is combined with bulky organoaluminium to construct Lewis pairs(LPs) for polymerization of VPs. The living/controlled polymerization of 4-vinylpyridine(4-VP) or 2-vinylpyridine(2-VP) can be accomplished with remarkable efficiency by such Lewis pair polymerization(LPP), furnishing polymers with high molecular weight(up to 288 kg/mol) and narrow molecular weight distribution(as low as 1.17). Mechanistic studies reveal the interaction of LPs and formation of zwitterionic intermediates, providing solid evidences to support the proposed polymerization mechanism. More importantly, by simply adjusting the LA dosage, this LPP strategy realizes the unprecedented control over the sequence regulation of 2-VP-based copolymers from gradient to block in one-step manner, regardless of the monomer ratio, which greatly expands the versatility of the LPP.

Predictive active control of building structures using LQR and artificial intelligence

This study presents a neural network-based model for predicting linear quadratic regulator(LQR)weighting matrices for achieving a target response reduction.Based on the expected weighting matrices,the LQR algorithm is used to determine the various responses of the structure.The responses are determined by numerically analyzing the governing equation of motion using the state-space approach.For training a neural network,four input parameters are considered:the time history of the ground motion,the percentage reduction in lateral displacement,lateral velocity,and lateral acceleration,Output parameters are LQR weighting matrices.To study the effectiveness of an LQR-based neural network(LQRNN),the actual percentage reduction in the responses obtained from using LQRNN is compared with the target percentage reductions.Furthermore,to investigate the efficacy of an active control system using LQRNN,the controlled responses of a system are compared to the corresponding uncontrolled responses.The trained neural network effectively predicts weighting parameters that can provide a percentage reduction in displacement,velocity,and acceleration close to the target percentage reduction.Based on the simulation study,it can be concluded that significant response reductions are observed in the active-controlled system using LQRNN.Moreover,the LQRNN algorithm can replace conventional LQR control with the use of an active control system.

Regulation of glucose and protein syntheses by Micrococcus luteus during the fermentation of a Nigerian rice, <i>Oryza sativa</i>variety “Igbimo”

Micrococcus luteus synthesises glucose and protein during the fermentation of a Nigerian rice. To regulate the formation of these substances, mutation was carried out with an alkylating agent: ethylmethyl sulphonate (EMS). Screening the mutants generated for the levels of the traits expressed, four major groups were obtained. These are poor, moderate, good and super producers of either glucose or protein. They produced the properties at 0 - 1.00, 1.01 to 1.99 (moderate) and 2.0 to 2.99 (good) and 3.0 and above (super) mg.mL–1 of each substance. The classes were made up of 37, 40, 20 and 3 mutants for glucose production and 13, 35, 40 and 12 mutants for protein synthesis. The wild strain bacterium made 0.86 mg.mL–1 glucose and 1.2 mg.mL–1 protein describing the M. luteus as poor glucose maker and moderate protein producer. It was also noticed that the mutation caused some variants (25%) to form more glucose than protein;the remaining 75% of the population are made up of two sets viz: mutants having better ability to synthesise protein at higher concentrations than glucose and those that formed about the same amounts of the substances. It thus follows that the glucose and protein productions in M. luteus are genetically based and can be regulated by genetic manipulation.

Learning control of fermentation process with an improved DHP algorithm

Control of the fed-batch ethanol fermentation processes to produce maximum product ethanol is one of the key issues in the bioreactor system.However,ethanol fermentation processes exhibit complex behavior and nonlinear dynamics with respect to the cell mass,substrate,feed-rate,etc.An improved dual heuristic programming algorithm based on the least squares temporal difference with gradient correction(LSTDC) algorithm(LSTDC-DHP) is proposed to solve the learning control problem of a fed-batch ethanol fermentation process.As a new algorithm of adaptive critic designs,LSTDC-DHP is used to realize online learning control of chemical dynamical plants,where LSTDC is commonly employed to approximate the value functions.Application of the LSTDC-DHP algorithm to ethanol fermentation process can realize efficient online learning control in continuous spaces.Simulation results demonstrate the effectiveness of LSTDC-DHP,and show that LSTDC-DHP can obtain the near-optimal feed rate trajectory faster than other-based algorithms.

Design of an Expert Control System for Biogas Fermentation Process

Controlling the biogas fermentation process is the key for maintaining stable operation of biogas system and increasing gas yield. Aiming at features of biogas fermentation process and difficulties of control, a practical control scheme is proposed combining the abundant experience of biogas experts. And it discussed the structural design and hardware configuration of the expert control system, established the database and role base, and designed the control strategy of production system inference. The design scheme with ZigBee and PDA technology as core is employed so as to solve problems of environmental factor detection and data transfer management. The test result shows that the deviation of temperature is controlled within ± 0.9℃, the deviation of pH is controlled within ±0.3, the deviation of oxidation-reduction potential is controlled within ±30mV, the deviation of gas production is controlled within ± 9mL and that of methane concentration is controlled within ±4.5%. This system is easily expandable and applicable to biogas engineering at various scales.

Structure Design of Large-scale Fattening Pig House with Fermentation Bed

The fattening pig house with fermentation bed had an area of 2 100 m2, and the area of fermentation bed was 1 900 m2 with a utilization rate of 91.4%, which was 45% higher than that of conventional pig house with surrounding barrier. There was feeding trough around the house. The water troughs were set in the middle of the fermentation bed and of the feeding trough on the short sides of the house, separating feed and water. There were electric aluminum alloy shutters in both long sides of the house for ventilation, cooling and heat preservation. On both short sides, there were fans and wet curtains. The spray cooling devices were in- stalled outside the roof for cooling. The environmental control in the piggery, includ- ing light, temperature, water, humidity, carbon dioxide and ammonia, was realized to run by computer automatically. The coconut chaff and chaff configuration were used as mattress material, realizing the advantages of fermentation bed, such as no smell, zero emission, high-quality meat, saving labor, controlling disease, no drug residue, producing fertilizer, intelligent control, mechanized operation, etc.

Study on the Liquid Fermentation Condition of Trichoderma hamatum Th12 Strain

[Objective] The research aimed to study the optimum liquid fermentation condition of Trichoderma hamatum Th12 strain,which provided the theory basis for studying and developing the biological control agent of Trichoderma spp.[Method] By using the diluted soil plate method and the plate confrontation method,Trichoderma hamatum Th12 strain was identified.Moreover,the influences of carbon source,nitrogen source,fermentation time,fermentation temperature,initial pH,inoculation amount,bottling capacity and shaker speed on the mycelium yield of Trichoderma hamatum Th12 were studied.[Result] The optimum liquid fermentation conditions of Trichoderma hamatum Th12 strain were：glucose as the carbon source,peptone as the nitrogen source,fermentation temperature was 25 ℃,initial pH was 6,inoculation amount was 0.8 ml,bottling capacity was 50 ml,shaker speed was 180 r/min.Trichoderma hamatum Th12 strain was cultivated 3 d under the condition,and the mycelium yield was the highest.[Conclusion] Under the optimum liquid cultivation condition,Trichoderma hamatum Th12 strain could provide the mycelium amount which was used for preventing and controlling Fusarium oxysporum and Rhizoctonia solani of lawn to the utmost extent.

Technical Regulations for Comprehensive Control Weeding in Millet Field

According to experiments in many years in Hebei Province, the research explored weed comprehensive control technology on millet fields in terms of application range, prevention rules, prevention object, agricultural control, physical control, and chemical control and proposed specific methods and technological indices in order to provide technological references for comprehensive control on weeds of millet fields.

Control design for the nonlinear benchmark problem via the output regulation method

The problem of designing a feedback controller to achieve asymptotic disturbance rejection / attenuation while maintaining good transient response in the RTAC system is known as a benchmark nonlinear control problem, which has been an intensive research subject since 1995. In this paper, we will further investigate the solvability of the robust disturbance rejection problem of the RTAC system by the measurement output feedback control based on the robust output regulation method. We have obtained a design by overcoming two major obstacles: find a closed-form solution of the regulator equations; and devise a nonlinear internal model to account for non-polynomial nonlinearities.

Asymptotic regulation of cascade systems with unknown control directions and nonlinear parameterization

A robust partial-state feedback asymptotic regulating control scheme is developed for a class of cascade systems with both nonlinear uncertainties and unknown control directions. A parameter separation technique is introduced to separate the time-varying uncertainty and the unmeasurable state from nonlinear functions. Then, the Nussbaum-type gain method together with the idea of changing supply functions is adopted in the design of a smooth partial-state regulator that can ensure all the signals of the closed-loop system are globally uniformly bounded. Especially, the system state asymptotically converges to zero. The design procedure is illustrated through an example and the simulation results show that the controller is feasible and effective.