REF615A_E?HAFAABAAABE1BCA1XE脈沖輸入模塊卡件

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主營DCS控制系統(tǒng)備件，PLC系統(tǒng)備件及機器人系統(tǒng)備件，
優(yōu)勢品牌：Allen Bradley、BentlyNevada、ABB、Emerson Ovation、Honeywell DCS、Rockwell ICS Triplex、FOXBORO、Schneider PLC、GE Fanuc、Motorola、HIMA、TRICONEX、Prosoft等各種進口工業(yè)零部件
REF615A_E?HAFAABAAABE1BCA1XE脈沖輸入模塊卡件

新一代DCS繼續(xù)向智能化、開放型方向發(fā)展。一些“標(biāo)準(zhǔn)化”的策略軟件，以適當(dāng)?shù)哪K方式,組合應(yīng)用于DCS之中，增強了系統(tǒng)性能。DCS的技術(shù)進步，使DCS廠家除提供比值、串級、前饋、PID等基本控制軟件外，還提供了一些先進過程控制(APC)軟件。如Honey-well公司將HMPC多變量預(yù)估控制器作為應(yīng)用模塊組合在TDC23000DCS系統(tǒng)中。日本橫河公司CENTUM系統(tǒng)控制策略中,包含了自整定控制、順序控制、批量控制、預(yù)估控制和模糊邏輯控制等等。
2 控制技術(shù)發(fā)展新方向 2.1 多變量預(yù)估控制多變量預(yù)估控制器（RMPC）可由多輸入多輸出、基于模型的、有預(yù)估能力和優(yōu)化功能組成，能夠控制和優(yōu)化高度耦合的石化生產(chǎn)過程。RMPC控制器與要控制的工業(yè)過程相結(jié)合，根據(jù)預(yù)估將來情況并決定怎樣調(diào)節(jié)控制器輸出把所有被控變量保持在設(shè)定的點上或約束范圍內(nèi)。多變量控制在石化行業(yè)中應(yīng)用范圍可從蒸餾塔的雙組分控制到FCCU兩器和主分餾塔的燃燒、苛刻度和產(chǎn)品回收控制,近年在天然氣加工、合成氨生產(chǎn)、時滯焦化分餾塔產(chǎn)品控制、加氫裂化、渣油加氫處理等領(lǐng)域中也有諸多應(yīng)用。 2.2 智能控制隨著社會生產(chǎn)的迅速發(fā)展，控制科學(xué)面臨著越來越多的挑戰(zhàn)，例如，要實現(xiàn)對越來越復(fù)雜且具有不確定性的對象進行有效的控制，要求所設(shè)計的控制系統(tǒng)具有學(xué)習(xí)能力、強魯棒性、實時性和柔性結(jié)構(gòu)，以及自組織與并行處理等智能化信息處理能力，這就是智能控制。人工神經(jīng)元網(wǎng)絡(luò)由于具有學(xué)習(xí)能力、自適應(yīng)、自組織、容錯與自修復(fù)等能力，使它在控制領(lǐng)域中的應(yīng)用收到了高度的重視，神經(jīng)控制已成為智能控制的一個重要發(fā)展方向[3]。化工生產(chǎn)過程中pH中和過程是一類有代表性的復(fù)雜化工過程，不同情況下，溶液pH值相對于加料量的變化非常大，具有明顯的非線性，
而且，在實際的反應(yīng)過程中還存在混合、測量等時滯環(huán)節(jié)，更加重了這一過程的復(fù)雜程度，傳統(tǒng)的非線性PID控制能將問題轉(zhuǎn)化到線性區(qū)域，然而一旦對象特性發(fā)生小的變化，已經(jīng)整定好的非線性補償環(huán)節(jié)就很難抵消pH過程的非線性特性影響，基于此，可將PID控制器與神經(jīng)元控制器相結(jié)合，用一個神經(jīng)元實現(xiàn)變結(jié)構(gòu)PID控制器中結(jié)構(gòu)變化的部分，同時用另一個神經(jīng)元實時調(diào)整PID控制器的參數(shù)，從而解決該問題。 2.3 人工介入控制石油化工的生產(chǎn)過程是一個復(fù)雜的人機綜合系統(tǒng)，在這個系統(tǒng)中，人、原材料、設(shè)備、工藝和環(huán)境是組成這個系統(tǒng)的基本要素[4]，人在這個系統(tǒng)中起主導(dǎo)作用。盡管系統(tǒng)的自動化程度提高了，但是還要由人來控制操作，要由人來負(fù)責(zé)設(shè)計、制造、組織、管理、維修、訓(xùn)練，要由人來決策，因此研究人與機器、人與環(huán)境及機器與環(huán)境之間的相互關(guān)系，把人的因素作為系統(tǒng)設(shè)計的重要條件和原則，為系統(tǒng)研究提供一種新的理論依據(jù)和方法。一般計算機和控制技術(shù)在石化公司的應(yīng)用可分為兩大部分：一是管理部分，即管理信息系統(tǒng)（MIS）；二是生產(chǎn)過程控制部分。人工介入控制系統(tǒng)的實質(zhì)是監(jiān)督控制，其重要功能在于優(yōu)化操作，結(jié)合石化企業(yè)運營實際，可將人工介入生產(chǎn)過程分為以下幾個過程：裝置監(jiān)督控制與優(yōu)化、優(yōu)化控制與先進控制、中央控制人員、常規(guī)過程控制、巡回檢查人員以及生產(chǎn)過程。通過采用計算機和通信技術(shù)，對石油化工生產(chǎn)過程的作業(yè)人員進行管理，將生產(chǎn)過程的人員監(jiān)控管理和過程控制融為一體。在石油化工生產(chǎn)過程中引入人工介入控制系統(tǒng)，可以達(dá)到優(yōu)化操作，提高企業(yè)經(jīng)濟效益的目的。 2.4 統(tǒng)計過程控制統(tǒng)計過程控制即SPC，主要是指應(yīng)用統(tǒng)計分析技術(shù)對生產(chǎn)過程進行實時監(jiān)控，科學(xué)的區(qū)分出生產(chǎn)過程中產(chǎn)品質(zhì)量的隨機波動與異常波動。隨著客戶的要求越來越苛刻，競爭對手的水平越來越高，如何保持過程生產(chǎn)中的穩(wěn)定，使質(zhì)量不合格的產(chǎn)品越少越好成為石化企業(yè)的當(dāng)務(wù)之急。SPC主要是通過各種控制圖來達(dá)到進行質(zhì)量分析、質(zhì)量控制和質(zhì)量改進的目的。SPC的核心工具是控制圖，如計量型控制圖(平均值-極差圖、平均值-標(biāo)準(zhǔn)差圖等)和計數(shù)型控制圖(不合格品率圖、缺陷數(shù)圖、單位缺陷數(shù)圖等)等，用來直接控制生產(chǎn)過程，進行質(zhì)量診斷和質(zhì)量改進，在生產(chǎn)過程中起到了預(yù)防為主的作用，正所謂：檢驗是一種浪費，只有預(yù)防才會創(chuàng)造價值。The new generation DCS continues to develop in the direction of intelligence and openness. Some "standardized" strategy software is combined and applied in DCS in an appropriate module way, which enhances the system performance. With the technical progress of DCS, the DCS manufacturer provides not only basic control software such as ratio, cascade, feedforward and PID, but also some advanced process control (APC) software. For example, Honeywell combines HMPC multivariable predictive controller as application module in TDC23000DCS system. The control strategy of CENTUM system of Yokogawa Corporation of Japan includes self-tuning control, sequence control, batch control, predictive control and fuzzy logic control.

2 New direction of control technology development 2.1 Multivariable predictive control The multivariable predictive controller (RMPC) can be composed of multiple inputs and multiple outputs, model-based, predictive and optimization functions, and can control and optimize highly coupled petrochemical production processes. The RMPC controller is combined with the industrial process to be controlled. According to the estimated future situation, it decides how to adjust the controller output to keep all controlled variables at the set point or within the constraint range. Multivariable control can be applied in petrochemical industry from two-component control of distillation tower to combustion, caustic scale and product recovery control of FCCU and main fractionator. In recent years, it has also been widely used in natural gas processing, synthetic ammonia production, delayed coking fractionator product control, hydrocracking, residue hydrotreating and other fields. 2.2 Intelligent control With the rapid development of social production, control science is facing more and more challenges. For example, to achieve effective control of increasingly complex and uncertain objects, the designed control system is required to have learning ability, strong robustness, real-time and flexible structure, as well as intelligent information processing capabilities such as self-organization and parallel processing. This is intelligent control. Due to its learning ability, self adaptation, self-organization, fault tolerance, self repair and other capabilities, the application of artificial neural network in the control field has received high attention, and neural control has become an important development direction of intelligent control [3]. The pH neutralization process in chemical production is a typical complex chemical process. Under different circumstances, the pH value of the solution changes greatly with respect to the feeding amount, which has obvious nonlinearity,
In addition, in the actual reaction process, there are also time delay links such as mixing and measurement, which aggravates the complexity of this process. The traditional nonlinear PID control can transform the problem into a linear area. However, once the object characteristics change slightly, it is difficult for the set nonlinear compensation link to offset the impact of the nonlinear characteristics of the pH process. Based on this, the PID controller can be combined with the neuron controller, One neuron is used to realize the structure change part of the variable structure PID controller, and another neuron is used to adjust the parameters of the PID controller in real time to solve this problem. 2.3 The manual intervention control of petrochemical production process is a complex man-machine integrated system, in which people, raw materials, equipment, process and environment are the basic elements of the system [4], and people play a leading role in the system. Although the degree of automation of the system has been improved, people still have to control the operation, design, manufacture, organization, management, maintenance and training, and make decisions. Therefore, the study of the relationship between people and machines, people and the environment, and machines and the environment takes human factors as the important conditions and principles of system design, providing a new theoretical basis and method for system research. The application of general computer and control technology in petrochemical companies can be divided into two parts: one is the management part, that is, management information system (MIS); The second is production process control. The essence of the manual intervention control system is supervision and control, and its important function is to optimize operation. Combined with the actual operation of petrochemical enterprises, the manual intervention production process can be divided into the following processes: plant supervision and control and optimization, optimization and advanced control, central control personnel, conventional process control, patrol inspection personnel, and production process. Through the use of computer and communication technology, the operators in the petrochemical production process are managed, and the personnel monitoring management and process control in the production process are integrated. The introduction of manual intervention control system in the petrochemical production process can achieve the purpose of optimizing operation and improving the economic benefits of enterprises. 2.4 Statistical process control Statistical process control (SPC) mainly refers to the application of statistical analysis technology to monitor the production process in real time and scientifically distinguish the random fluctuation and abnormal fluctuation of product quality in the production process. With the increasingly demanding requirements of customers and the higher level of competitors, how to maintain the stability in the process of production and make the number of unqualified products less and better has become a top priority for petrochemical enterprises. SPC is mainly used for quality analysis, quality control and quality improvement through various control charts. The core tools of SPC are control charts, such as metering control charts (average range chart, average standard deviation chart, etc.) and counting control charts (nonconforming product rate chart, defect number chart, unit defect number chart, etc.), which are used to directly control the production process, carry out quality diagnosis and quality improvement, and play a preventive role in the production process. It is the so-called: inspection is a waste, and only prevention can create value.

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