*Proceeding Paper* **An Intelligent Optimization Algorithm for Scheduling the Required SIL Using Neural Network †**

**Naoual Batout \*, Riad Bendib \* and Youcef Zennir \***

Department of Petrochemical and Process Engineering, University of Skikda, Skikda 21000, Algeria

**\*** Correspondence: n.batout@univ-skikda.dz (N.B.); r.bendib@univ-skikda.dz (R.B.);

y.zennir@univ-skikda.dz (Y.Z.)

† Presented at the 2nd International Conference on Computational Engineering and Intelligent Systems, Online, 18–20 November 2022.

**Abstract:** The purpose of safety analysis is to ensure that hazards and risks that could be a possible source of harm and damage are reduced well enough by dealing with all phases of the safety lifecycle and design of suitable safety barriers. It is known that any error or failure to perform the function of each proposed safety barrier can cause extreme damage to the environment, facilities and humans, and even loss of life. Therefore, it is necessary to ensure the effectiveness of the study or analysis. However, even with the major development in control system fields the problems of uncertainties, classification and optimization are still considered unsolved issues. In recent years several tools are developed based on artificial intelligence to deal with such difficulties. In this work, an approach based on Artificial Neural Networks (ANN) is developed to schedule the SIL values of the safety integrity functions (SIF) of an industrial-fired heater. The SIFs are first deduced from HAZOP study for the fired heater. The SIL risk of the consequences related to personnel health and safety, the economic SIL and environment SIL are considered as inputs of the multilayer network with a predefined hard limit activation function.

**Keywords:** optimization; ANN; hard limit; Safety; SIL; HAZOP; fired heater

#### **1. Introduction**

Artificial Intelligence has a broad variety of application some of which we already know and encounter in our everyday life: spam filters recognizing malicious emails, search engine filters finding the "best results", vacuum cleaner robots or even no playable characters in video games [1,2].

The assumption that the human brain may be deemed quite comparable to computers in some ways offers the spontaneous basis for artificial intelligence (AI) [3,4].

The concept of AI was introduced following the creation of the notion of the Information Technology (IT) revolution, and is an attempt to replace human intelligence with machine intelligence [5]. According the Oxford dictionary, the word intelligence is derived from intellect, which is the faculty of knowing, reasoning and understanding. Intelligent behavior is, therefore, the ability to reason, plan and learn, which in turn requires access to knowledge.

AI requires a myriad of techniques, the most important of which is:

✓ artificial neural networks that rely on recognition system based on machine learning/deep learning to perform learning from observational data and discover their solutions [6].

#### **2. Artificial Neural Networks**

Artificial neural networks (ANNs) set out to emulate their biological equivalent. The simple model of neuron was proposed by MCCULLOCH and PITTS (1943), and HEBB

**Citation:** Batout, N.; Bendib, R.; Zennir, Y. An Intelligent Optimization Algorithm for Scheduling the Required SIL Using Neural Network. *Eng. Proc.* **2023**, *29*, 5. https://doi.org/10.3390/ engproc2023029005

Academic Editors: Abdelmadjid Recioui, Hamid Bentarzi and Fatma Zohra Dekhandji

Published: 11 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

(1949) described a technique that became known as 'HEBBIAN learning'. ROSNBLATT (1961) developed a single layer of neurons called perceptron, which was used for optical pattern recognition [7].

WIDROW and SMITH (1964) purposed the first applications of this technology for control purposes. They developed an adaptive linear element (ADLINE) that was taught to stabilize and control an inverted pendulum. The back propagation training algorithm was investigated by WERBOS (1974) and further developed by RUMELHART (1986) and others, leading to the concept of the multi-layer perceptron (MLP) [8].

Artificial neural networks have the following potential advantages for intelligence control:


#### *2.1. The Formal Neuron*

A formal neuron simply performs a weighted sum of those inputs, adds a threshold to that sum, and passes the result through a transfer function (activation function) to obtain its output like Figure 1 indicates [11].

**Figure 1.** Formal neuron structure.

where:

$$Y = f(\sum\_{j=1}^{n} wj \ xj - b) \tag{1}$$

#### *2.2. Multi-Layer Networks*

In this case, the networks generally have at least three layers, an input layer, one or more hidden layers and an output layer. Information flows from input to output through the hidden layer(s) as in Figure 2 [12].

**Figure 2.** Multi-layer network structure.

#### *2.3. Activation Function*

Weighted input w and the bias b are summed up to create the transfer function net input, which is once more a scalar. This sum is the argument of the transfer function f.

f is a step function or a sigmoid function. Note that the neuron's scalar parameters w and b are both adjustable [1,12].

The fundamental concept behind neural networks is that these parameters can be changed to prompt the network to behave in an interesting or desired way. By adjusting the weight or bias parameters, we can instruct the network to perform a certain task. Alternatively, the network itself will adjust these parameters to achieve some desired end.

The shapes most used are presented in Figure 3.

**Figure 3.** The function shape: (**a**) hard-limit function (Heaviside function) all or nothing (Neuron of Mac Culloch and Pitts (1945)); (**b**) linear function; (**c**) sigmoid function; (**d**) Gaussian function.

#### *2.4. Neural Networks Learning*

The learning consists of calculating the parameters in such a way that the network outputs are as close as possible to the "desired" outputs, which can be the code of the class to which the form that we want to classify belongs, the function value that we want to approximate or that of the process outputs that we want to model, or even the desired output of the process to be controlled [5,13].

Formal neural network learning techniques are optimization algorithms; they seek to minimize the gap between the network's actual responses and the desired responses by changing the parameters in successive steps (called "iterations"). The network output fits the data better and better as the training proceeds. However, the error made by the neural network at the end of learning is not zero [14,15].

Basically, there are two types of learning, unsupervised learning and supervised learning:


#### **3. Safety Integrity Level (SIL)**

Safety instrumented systems (SIS) are technical systems that are widely used in the process industry. The mission of SIS is to detect the onset of hazardous events and to protect humans, material assets and the environment from their consequences. An SIS can perform several safety instrumented functions (SIF) and it is considered as an independent protection layer (IEC 61508 2010) [19].

A safety function is, thus, expressed in terms of the action to be taken and the required probability to satisfactorily perform this action [20].

As a quantitative target, this probability establishes the safety integrity [21]. The IEC 61508 defines four distinct safety integrity levels, SIL1, SIL2, SIL3 and SIL4, and the quantitative targets to which they are associated depend on whether the safety-related system is operating continuously or in low demand mode, for example, a shutdown system. The PFD or its inverse, the risk reduction factor, is the proper indicator of the first situation's safety function performance (RRF). Concerning the probability of a hazardous failure every hour is a function that runs constantly [22–24].

The four SIL definitions for low demand mode are shown in Table 1. As demonstrated, the more accessible the safety-related system is the higher SIL, and the more stringent becomes the implementation of safety function.


**Table 1.** Definitions of SILs for low demand mode.

For determining the SIL, IEC standards have provided various methods that have been applied with differing degrees of success. These methods range from using pure quantitative risk assessments (QRAs) to more qualitative methods, as follows:


The issue under this study is to classify the overall SIL for the deduced SIFs in HAZOP study (Table A1) [25].

The risk matrix used to identify SIL of different deduced SIFs takes into account the following: consequences related to environmental impact; consequences connected to production and equipment loss; consequences related to personnel's health and safety, is presented in Table 2 [20,26].


**Table 2.** Risk matrix.

where: S0, ... , S5 are categories of consequences on the health and safety of personnel (Table A2); E0, ... , E5 are environmental consequences categories (Table A3); L0, ... , L5 are economic consequence categories (Table A4).

#### **4. Application**

In this paper, methodology based on artificial neural networks is presented for the fired heater F201-101 of the crude distillation unit of ADRAR refinery Algeria represented in Figure 4. The unit is a part of ADRAR refinery, located in the south of Algeria.

**Figure 4.** Process diagram for crude distillation unit.

Any fired heater should, in general, be controlled for the following parameters, as in Figure 5:


**Figure 5.** Process diagram for crude distillation unit.

The taken SIFs from [25], which were deduced based on the HAZOP study from [25] with their SILs, are included in the Table 3 (the possible scenarios in case SIF fails are summarized in Table A1.)

**Table 3.** SIL matrix values for each SIF.


The main objective for this study is to schedule the SILs values to the required SIL for the SIFs presented in Table 4, for this reason we have applied an optimization algorithm using a multi-layer artificial network (Figure 6).

**Figure 6.** Network structure for the studied algorithm.

Inputs *x*1 and *x*2 to the neuron are multiplied by weights *w*1 and *w*2 and summed up together. The resulting *n* is the input to the activation function *f*. The activation function was originally chosen to be a relay function, but for mathematical convenience a hard-limit function is used; it is defined as

$$f = \begin{cases} \ge 1 \text{ if } w\infty > 0\\ \ge 2 \text{ if } w\infty < 0 \end{cases} \tag{2}$$

The output of the first node becomes an input for the second node.

We used this function in our algorithm to create neurons that make classification decisions, and the typical network is shown in Figure 6.

The following table represents the network parameters.

**Table 4.** Network parameters.


#### **5. Results and Discussion**

The aim of this work is to create a cognition and decision system that classify the SILs values with a predefined activation function to define the overall SIL or the required SIL.

The work is conducted using MATLAB and results are presented in the below table (Table 5).

**Table 5.** SIFs deduced from HAZOP study.


As it is shown in the table, the safety integrity level of the heater's safety instrumented function are classified. The next step to ensure the safety of the fired heater is to compare the obtained results with the calculated SIL resulted from the calculation of the equivalent probability failure under demand of the corresponding safety integrity system. Depending on this comparison, recommendations for the safety system design are raised (i.e., keeping the existing component or proceeding to design configuration in case the calculated SIL is smaller than the required SIL) [25].

The parameters of the considered ANN are obtained during the learning step and they are suitable to be used in any complex system, as in the case of petrochemical plants [27].

**Author Contributions:** Conceptualization, N.B. and R.B.; methodology, N.B.; validation, N.B. and R.B.; formal analysis, N.B.; investigation, N.B.; resources, N.B.; data curation, R.B.; writing—original draft preparation, N.B.; writing—review and editing, N.B., R.B. and Y.Z.; visualization, N.B., R.B. and Y.Z.; supervision, R.B. and Y.Z.; All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**


**Table A1.** SIFs deduced from HAZOP study.

**Table A2.** Personnel safety and health categories.


**Table A3.** Environment consequences categories.




#### **References**


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