84195 - Process Safety Engineering M

Learning outcomes

After the course students will be able to assess the risks due to industrial installations (i.e. chemical and process industries), through the application of basic concepts about: classification of hazardous substances, hazard identification, probabilistic assessment of top events and consequence assessment.

Course contents

REQUIREMENTS - PRIOR KNOWLEDGE

First of all, the student should possess the basic skills relating to mathematics that are typically acquired during high school, as well as those relating to the English laguage (optimally C1, but at least B2 level knowledge, according to the Common European Framework of Reference for Languages (CEFR)):

 

1. with reference to the English language, it is advisable to be able:

- to understand complex texts and specialized topics;

- to express oneself fluently and precisely, even in a formal context;

- to write with coherence, clarity and precision.

2. with reference to mathematics, it is in particular appropriate for the student to be able to easily solve algebraic and transcendent equations, also with the aid of a scientific calculator.

 

In addition, the student should possess the skills that are typically acquired during first cycle degree courses in engineering, i.e., an adequate mastery of the knowledge and tools of the basic sciences applied to engineering as well as the methodological-operational aspects of engineering, with particular regard to engineering modelling.

 

COURSE CONTENS for a.y. 2026-2027

1) Introduction to loss prevention and risk analysis

Risk and risk assessment. The risk assessment procedure. Risk matrixes. Risk acceptability criteria. Risk mitigation. Introduction to relevant regulation.

2) Hazardous properties of substances

Introduction to the hazardous properties of materials. Flammability. Toxicity. The GHS classification system. The material safety data sheet. Labelling.

3) Hazard identification

Introduction to hazard identification. Hazard identification techniques: past accident analysis analysis; checklists; safety review; index methods; what-if analysis; FMEA / FMECA; HazId analysis, HazOp analysis. How to choose the most suitable technique.

4) Consequence and damage assessment

Introduction to consequence assessment models. Source models: introduction; typical storage conditions of substances; schematization of the releases; some source term models (liquid outflow, gas outflow, flash, evaporating and boiling pool models). Fire models: introduction to fires in the process industry and to their modeling; radiation models for poolfires, jetfires, fireballs. Dispersion models: introduction and classification of models; meteorological parameters (atmospheric turbulence and wind); Gaussian dispersion model for steady state releases (isopleths for flammable and toxic clouds, mass in the explosivity range); Gaussian dispersion model for instantaneous releases; dispersion of heavy gases. Explosion models: classification of explosions (physical explosions and BLEVEs, chemical explosions, UVCEs); the TNT model; TNT models for physical explosions and UVCEs; consequence assessment of VCFs. Post-release event trees. Damage models for heat radiation, overpressure and toxic exposure (thresholds and probit models).

5) Frequency evaluation and reliability engineering

Introduction to frequency evaluation and reliability engineering. Standard frequency values and parts count. Elements of Boolean Algebra. Reliability of systems: Fault Tree Analysis. Quantified event trees. The bow-tie diagram.

6) Risk recomposition

Local risk. F-N curves.

 

Within each section, knowledge of the concepts presented in the introductory part, as well as knowledge of the relevant physical quantities and of the variables on which each of them depends, together with the ability to set up the mathematical modelling of processes and systems, is fundamental. Less importance is assigned to the solution of numerical exercises.

Readings/Bibliography

For further study of the various topics covered during the lectures (although not necessary to pass the examination with full marks) the following books may be consulted:

• Lees' Loss Prevention in the Process Industries, S. Mannan editor, IV ed., Butterworth-Heineman, Oxford, UK, 2012
• R.Rota, G. Nano, Introduzione alla affidabilità e sicurezza nell'industria di processo, Bonomo Ed., Bologna, I, 2024
• D.A.Crowl, J.F.Louvar, H.R.Flodman, T.Carter, C.Mashuga, Chemical process safety: fundamentals with applications, V ed., Pearson Education, USA, 2026
• Centre for Chemical Process Safety of AIChE, Guidelines for chemical process quantitative risk analysis (II ed.), New York, USA, 1999
• Center for Chemical Process Safety of AIChE, Guidelines for hazard evaluation procedures (III ed.), AIChE, New York, USA, 2008
• TNO, Methods for the calculation of physical effects (Yellow book). Report CPR 14E (III ed.), The Hague, NL, 2005
• H.Kumamoto, E.Henley, Probabilistic Risk Assessment and Management for Engineers and Scientists, 2nd edition, IEEE Press, New York, 2000

All these books (in some cases in one of the previous editions) can be found at the Library F.P.Foraboschi in via Terracini 28; for information about the availability of the books, please contact the librarian (Annalisa Neri, annalisa.neri@unibo.it). Furthermore, some of these texts, which are freely available online in electronic format, are made available on Virtuale as part of the teaching materials.

Teaching methods

The teaching methods adopted in the PSE M course are based on student-centred learning.

The learning process for the PSE M course is centred on lectures delivered by the teacher, with explanations of the course’s theoretical content and the presentation of numerical exercises designed to illustrate how these theoretical concepts are concretely applied in risk analysis. However, the PSE M course is focused mainly on theory, since practical activities (e.g., use of software for consequence assessment) are offered in the Laboratory of Process Safety M – prof. A.Tugnoli). The students are continuously involved through questions aimed at the critical analysis of the theoretical content and the numerical results obtained, in order to promote active participation in the lessons themselves.

During the classes, optional homework assignments are given, to be completed individually, with the aim of fostering understanding, in-depth study, and reworking of the content presented in the lectures, while progressively developing reflective ability and autonomy. Before the next lesson, the teacher is available in the classroom well in advance to clarify doubts and help resolve difficulties encountered while carrying out the assignments. The homework assignments include, among other things, several real case studies in which risk analysis is applied in all its phases.

During the lessons, some recent major accidents will be presented through videos and discussed, in order to foster understanding of the physical phenomena involved and their mathematical modelling, the identification of the causes of the accidents themselves, and the safety measures that would have prevented their occurrence or mitigated their consequences.

Promoting an inclusive learning environment requires the constructive contribution and willingness of everyone involved to engage, that is, both the teacher and the students. The teacher is committed to promoting an inclusive learning environment in which everyone can participate in the lessons and engage in individual or small-group study under the best possible conditions, with respect for each person’s individual characteristics and within the framework of the University’s guidelines, which recognize in-person participation as the primary mode of teaching and learning activities. Without prejudice to any communications sent to the teacher by the University, for example by the Service for Students with Disabilities and Specific Learning Disorders, any specific teaching needs may be reported to the teacher from the beginning of the course, with due respect for confidentiality. By way of example, and without being exhaustive, specific teaching needs include gaps in prerequisites, difficulties in understanding the explanations provided during lessons or in carrying out the assigned activities, as well as the presence of physical, digital, linguistic, or organizational barriers. Such notifications may make it possible to identify, where reasonably possible and in any case within the framework of the University’s provisions and the guidelines of the Degree Programme, personalized strategies aimed at supporting the achievement of the learning objectives of the PSE M course.

All students are very strongly encouraged to attend the lessons in person with attention and continuity, from beginning to end, taking notes, preferably directly on the slides explained by the teacher and made available before the lessons, asking questions during the lessons, and completing the homework assignments.

Assessment methods

The PSE M examination is aimed at verifying the achievement of the knowledge and skills mentioned above, in accordance with the Dublin descriptors for the second cycle of university studies. In particular, students will be required to demonstrate that they are able to:

• know and understand the theoretical contents of the course;

• apply such knowledge to the analysis and solution of case studies;

• integrate different types of knowledge, formulate autonomous judgements, and address real-world problems, even in multidisciplinary contexts;

• communicate clearly, rigorously, and unambiguously, adequately justifying their statements;

• have acquired self-directed learning skills, useful for deepening and extending the competences developed in the PSE M course in their subsequent professional activity.

The exam consists in a written test, specifically a quiz with multiple choice answers, open questions, simple numerical exercises, questions with words to fit in, true or false questions,.... Sets of quizzes for each topic and an example of the written test (with its solution) are available in the teaching material. More information on the written test is reported in the teaching material and given by the teacher during the classes.

Passing the examination is possible for students who demonstrate knowledge of the hazardous properties of chemical substances, the measures used to quantify the risk of major accidents, the sequence of the different phases of quantified risk analysis, the main mathematical models, as well as the meaning and units of measurement of the most important quantities involved in each phase, while also being able to solve simple numerical exercises. A higher mark is awarded to students who demonstrate that they have understood and are able to use all the course contents, presenting them with appropriate terminology, identifying the interconnections among the contents themselves and setting up more complex problems. Failure to pass the examination is generally attributable to lack of knowledge of several parts of the course contents, superficial and incomplete presentation of the topics, inability to distinguish one accidental scenario from another, lack of knowledge of the methods for estimating the frequencies of accidental scenarios, and inability to solve numerical exercises. In any case, students will be penalised if they are unable to independently retrieve information about the PSE M course where such information has been made available to all students, as well as if they send the teacher emails containing questions whose answers are provided on the teacher’s and PSE M course webpages, on the Virtuale platform, and in the slides presented during the first lecture.

Students with Specific Learning Disorders (SLDs) or disabilities are advised to contact the relevant University office in good time (https://site.unibo.it/studenti-con-disabilita-e-dsa/it). The office will be responsible for proposing any appropriate accommodations to the students concerned; these accommodations must, in any case, be submitted to the teacher for approval at least 15 days in advance. The teacher will assess their suitability also in relation to the learning outcomes of the course.

During the examination, the use of artificial intelligence is prohibited, as is any access to the internet and the use of any electronic device other than a simple scientific calculator with no file-storage functions; any such use constitutes a violation of academic integrity.

The examination may be taken on one of the 6 annual examination dates set by the teacher and published on AlmaEsami, by registering for the examination session on AlmaEsami.

In order to avoid annoying the teacher, it is strictly forbidden to “try” the examination. To test one’s preparation, students may use the exercises in the course slides, the homework assignments, the quizzes, and the sample exam test. Students should sit the examination only when they can revise the course slides accurately and fluently and when they can solve the exercises correctly and quickly.

Further information about the examination is available in the PSE M examination regulation and in the introductory course slides, both available on Virtuale. Specifically, the PSE M exam regulation provides detailed information on the characteristics, on the duration and the evaluation criteria of the exam, as well as on the tools permitted during the exam. For anything not explicitly mentioned on the PSE M course webpage or in the PSE M examination regulation available on Virtuale, the provisions of the University Teaching Regulations shall apply.

Teaching tools

Personally taken lecture notes.

Material made available by the teacher [available from the start of the lessons for one calendar year on the Virtuale e-learning platform; access restricted to students who have the PSE M course in their study plan for academic year 2026/2027 or for one of the previous years]:

• slides used by the teacher during the lessons

• homework assignments

• summary of formulas for numerical exercises and for the written exam test

• supplementary documents

• supplementary video materials

• quizzes

• example of a written exam test and of its solution

The teacher does not make video recordings of the lessons available.

Artificial intelligence (AI), particularly generative artificial intelligence (genAI), may represent a useful tool to support individual study. In the homework assignments, specific guidance will be provided on its use as a support for understanding, personal reworking, and self-assessment of one’s own learning, while respecting students’ critical autonomy and fostering a conscious and responsible use of AI. The educational objective underlying the guidance on the use of AI in studying is to help students understand that AI is a tool that presents both opportunities and risks, and that it should be placed at the service of students as a support to their learning, without replacing their creative and intellectual activities, in line with the University Policy on genAI.

Office hours

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