Using information technology (IT), the multidisciplinary scientific field of environmental protection is expanding quickly. Exploring the intricate problems in environmental protection requires the discovery, integration, and analysis of vast amounts of diverse data. Environmental informatics converts environmental data into knowledge and information by providing methods and tools for managing it. The quality of data and research depends on information technology advancement, which also offers a solid foundation for future advancements. The simulation modelling aims to optimise the wastewater purification process and design methodology per important contaminated matter behaviour patterns [1].

The literature thoroughly describes models for presenting and analysing systems, including simulation models of discrete events, models of differential equations in continuous time,models of differential equations with discrete time, and operations research techniques. A dynamic approach is also used to analyse the overall behaviour of technical systems [2].

The selection of a methodology for the study and design of continuous-discrete water treatment processes is quite delicate. The process modelling approach is the foundation for system behaviour analysis, modelling, and optimisation. While modelling enables the creation of technical systems models irrespective of the variety of behaviour of their constituent parts, this method guarantees the accurate description of the system's structure, entities, and feature interactions. Other publications on this topic contain studies on model selection and process modelling [3]. Simulation modelling encompasses strategies and tactics for creating models of both continuous and discrete systems and the mechanism of moving from one component stage to the next, so it ensures that the right model design is chosen [4].

Also, simulation modelling can display the system at various levels. Conceptual models offer a presentation of the lawfulness of system behaviour and structure, thus allowing the study of the most important parameters of the functioning of the entire system or its components. Using simulation modelling, one can explore the legality of the system's behaviour, the interdependence of its entities, and the development of a system dynamics model. In this way, simulation modelling is a powerful tool for analysing the state, alignment parameters and the selection of the appropriate mode of operation of technical systems [1]. This paper focuses on modelling the purifying systems for wastewater from the oil industry.

Valuable information is obtained from using the model about the options for solving the problem, all versions of the possible solution, and potential negative impacts and states in which the system can find itself when specific characteristics of the system change [5]. Additionally, the system can be shown at different levels. Conceptual models allow the study of the most crucial aspects of how the complete system or its constituent parts operate by presenting the legality of its behaviour and structure. Simulation modelling is an effective technique for evaluating alignment parameters and the system state and choosing its best operating mode.

The complexity and quantity of data grow geometrically as the number of model elements increases [6], and the relationships between them include logical and mathematical relationships that interact [7]. The basic idea of simulation modelling is to transfer principles and experiment with objects that cannot be or would be too expensive or unethical to subject them to an experiment. The model is used to test new ideas, new scientific hypotheses or claims. Experimenting on a simulation model is, in most cases, cheaper than on a real system. In this paper, special attention will be paid to the conceptual and computational model of the wastewater purification system contaminated with a mixture of hydrocarbons, i.e. this article focuses on modelling purification systems for the wastewater contaminated with hazardous substances located in the Sovjak pit.

The Sovjak pit is a karst sinkhole with extremely steep edges (slopes), more than 30 m deep and with a circumference of around 90 m. It was used to dispose of hazardous waste, completely altering its nature over time. The county of Primorje-Gorski Kotar is where the Sovjak pit is located approximately 4 km from the sea and about 10 km northwest of the city of Rijeka, in the Viškovo municipality (Figure 1). The Sovjak pit is only 30 m away from the closest dwellings, and its immediate vicinity is heavily populated. The landfill is surrounded by heavily developed karst terrain, which suggests the presence of a sinkhole and deep depression with a depth of more than 40 m. The disposal of hazardous garbage in the Sovjak pit lasted more than 30 years before landfill closure in 1990.

Figure 1.

Cartographic representation of the location [8]

Approximately 150,000 m³ of hazardous garbage landed between 1956 and 1990 in the Sovjak pit. For the first ten years, the Rijeka Oil Refinery in Mlaka used the landfill only to dispose of the waste tar created while producing lubricants, motor oils [9], and asphalt [10]. Tar can solidify into gudron (hard tar) because of its characteristics [11]. Later, in addition to the tar, other hazardous wastes were dumped at the landfill, albeit in much lower quantities. Coke oven tar [12], acetylene sludge from shipyards [13], crude oil and petroleum products from tanks, oil residues, other petrochemical wastes [14], wastewater from cleaning tanks, solvents, waste cutting oils [15], and subpar items from customs are a few examples of other wastes in this category. Tar fills the sink's lower portion, and a mixture of various sorts of hazardous waste is above the tar layer. Several physical and chemical processes brought about by mixing waste components resulted in the formation of multiple strata of rubbish in the Sovjak pit, each with distinct physical and chemical characteristics:

1. Layer of floating oil 7000 m³,

2. Accumulated wastewater 15,000 m³,

3. Soft tar and sediment 55,000 m³,

4. Hard tar 75,000 m³.

Figure 2 and Figure 3 show the layers formed in the landfill, starting from its surface towards the bottom.

Figure 2.

Conceptual model of the Sovjak landfill and the different stages of waste separation [8]

Figure 3.

3D model of the waste disposed of in the Sovjak pit [8]

Industrial activities like extracting and processing fossil fuels pose environmental threats from oil pollution. When compounds tainted with oil are released into the environment, problems are caused worldwide [16]. The environmental impacts of petroleum are often harmful due to their toxicity to almost all lifestyles and may contribute to climate change. The human body is at risk from oil pollution in the air and water. A significant issue is how to remove oily effluent without harming the ecosystem [17]. Table 1 shows the characteristics of the wastewater layer.

An interdisciplinary scientific field called environmental protection significantly relies on information and communication technology (ICT) to analyse heterogeneous data and study challenging ecological problems. Simulation modelling is a standard methodology for parameterising and optimising processes [26]. Continuous-discrete water purification system analysis, modelling, and optimisation are based on describing the structure, entities, and relationships of system features [3]. As the foundation for creating a system dynamics model, mathematical models offer insight into the morality of system behaviour and the interdependence of entities [27].

The Sovjak pit location is one of the so-called high-risk areas ("black spots"). According to the Republic of Croatia's Waste Management Strategy, such areas are highly contaminated with hazardous wastes that pose a genuine risk to public health and the environment. They were brought about by the long-term improper management of industrial (technological) hazardous waste. The Sovjak pit, a location highly polluted due to such waste, is an example of a point source of pollution that directly affects the soil pollution in the area. Polluted water seeps through the pit's walls and into the subsurface, bringing hydrocarbons to the surface in a layer correlating with the amount of precipitation that falls there each year. The local populace frequently complains about the overpowering, disagreeable smell of old, deteriorated oil, which can travel more than 100 metres on a strong wind. Furthermore, the Sovjak pit is situated in a seismically active area and inside the catchment of coastal springs in the upper section of the Gulf of Rijeka, bordering the city of Rijeka's spring catchment. It is a tectonically highly complex region with complex structural and lithological linkages, featuring various fissures and scratches in the terrain where polluted water can migrate.

Rehabilitating the Sovjak pit is crucial for the community and the ecosystem because of everything mentioned above. The model's architecture can accommodate changes in independent, dependent, and transitional variables. It makes risk prediction possible, including the identification of probable failure points of the system that could result in material loss, environmental damage, or adverse effects on human health.

Goals of the research:

1. Creating, optimising, and validating the theoretical adaptive model of a system that is "sensitive" to modifications in the operational environment,

2. Creating and verifying a model of an organic hydrocarbons-cleaning water treatment system that can be used to cleanse contaminated water before its release into the environment,

3. Developing a research technique that established the hypothesis for studying intricate engineering systems: introducing feedback from the newly created adaptive model lowers the process's variability.

The purification of the water layer must be preceded by the extraction of solid, floating, bulky waste and the layer of floating hydrocarbons.

An object system, being a component of the real world, is described by a conceptual model [28]. Specifying the object system's data structure and the conditions for using the data serve as the foundation for conceptual modelling. Computer models are built on conceptual models based on ideas about the structure and logic of the system or issue being modelled [29]. A flowchart (Figure 4) and a process flow diagram (PFD, Figure 5) helped to develop the activity cycle diagram (ACD, Figure 6).

Figure 4.

Flowchart of the system for purification of water from hydrocarbons (designed by the authors)

Figure 5.

Process flow diagram of the system for purification of water from hydrocarbons (designed by the authors)

Symbol	Term	Symbol	Term
CP	centrifugal pump with capacity from 0.5 m3∕h to 10 m3∕h	1	wastewater from PK - devices for the treatment of floating hydrocarbons and soft tar
DP3	dosing pump – flocculant
DP4	dosing pump – coagulant	2	scrubber waste water S1 - devices for the treatment of floating hydrocarbons and soft tar
DP5	dosing pump – neutralizing agent
DP6	dosing pump – equalizer
U2	device for treatment of accumulated wastewater with a capacity of 85 m3	3	centrifugal separator (CS) wastewater
		4	drainage wastewater
		5	wastewater
S2	gas removal device	6	water from work surfaces
A	analyzer	7	tank wastewater
V	valve	8	unpurified water
PS2	temporary covered waterproof tank capacity 1.5 m3	9	return of water from scrubber
		10	PSB precipitate
PSB	temporary storage - réception pool of water for purification 85 m3	11	precipitate
		12	discharge of processed wastewater
CS	centrifugal separator	13	input signais of sensors
SPU	temporary wastewater tank	14	output signais, pumps and valves
GAC	activated carbon filter	15	compressed air
		16	discharge into the recipient

Figure 6.

Activity Cycle Diagram (ACD) of the system for purification of water from hydrocarbons (designed by the authors)

A flowchart is a block diagram that shows the order of choices and actions necessary to complete a process. A block for each stage in the sequence is noted. Connecting lines and directing arrows join the steps together, making it possible to view the flowchart and understand how the process works from start to finish.

A process flow diagram (PFD, Figure 5) is a sort of flowchart that shows how essential parts of the operation of an industrial facility relate to one another. Though its concepts are occasionally applied in other engineering activities, it is most frequently utilised in chemical engineering and process engineering to model new processes, enhance existing ones, and document existing ones.

An activity cycle diagram (ACD, Figure 6), a network model of the logical and temporal links among the activities, is used in activity-based modelling to depict the system's dynamics. The activity scanning approach of simulation execution makes it simple to create an ACD [1]. It is a graphical modelling tool describing how items interact in a system's activity cycle and explaining action sequences to cope with various real-life situations.

Modelling the system described by all its parameters was done through multiple software systems with iteration extremes. Since the body of the device itself does not form a whole, it was necessary to model the entire system, taking account of all technical and technological parameters that affect the performance of wastewater treatment, modelling also parts of the equipment that together form one functional unit.

For this purpose, the matrix computer package MIKE3 (the sequential computer package for internal use of the MIT institute) and the final confirmation via the computer packages Hydroflow or FlowS were used. All the mentioned computer packages are considered reliable. They are widely applied when modelling various wastewater treatment devices, suitable for handling municipal wastewater and industrial effluents highly contaminated with carcinogenic compounds [30]. The calculations are based on all known postulates of fluid mechanics; that is, the ideal-fluid representation of the flowing media is abandoned in favour of the real-fluid representation.

The three-dimensional model MIKE 3 is a general non-hydrostatic numerical modelling system. The hydrodynamic (HD) module is the basic module in the MIKE 3 Flow Model. It simulates unsteady three-dimensional flows, considering density variations, turbulence including buoyancy effects, friction level and/or velocity, particle tracking, etc. The advantage of this kind of work is that it removes the error of classic calculations that are not based on 3D systems but are limited to 2D, thus increasing the error in dimensioning and design [30].

The experimentation with the model included studying the dependence between the flow velocity field and the shape of the distribution blades. A total of six flow deflectors/blades were placed inside the tank, three on each side of the tank, whose dimensions are 50 cm χ 20 cm². The three most commonly used flow deflector models were analysed and compared: convex, concave, and vertical.

Partial results of flow deflector modelling are shown below. The modelling is carried out in a dynamic system, i.e., simulating the flow of wastewater inflow through 40,000 steps, where each step essentially represents 30 seconds of work. The above approach is also professionally called 40k-hm/f (40,000 steps lasting half a minute with a blocked frame). It is important to emphasise that all computer models stop iterating the results if any of the working medium's input parameters exceeds any value set as fixed (flow rate, type of material, density, temperature, system dilation, etc.). The iterative numerical modelling is performed until all 40,000 steps are correctly presented and adopted as realistic as possible. Thus, the possibility of error in the above modelling is minimal.

Figure 7 shows a legend for value gradients. Gradients show the direction and magnitude of the fastest increase in a quantity or function in space. Figure 7 shows a colour palette that describes the listed and displayed physical parameters, with blue showing the lowest values and red showing the highest values.

Figure 7.

Value gradients

Figure 8 shows the flow velocity distribution in the fluid around three blade shapes:

• Convex Blade Shape: Velocity is highest at the perimeter, decreasing to zero in the sheltered section, a stark contrast between high-speed edges and low-speed centre.

• Vertical Blade Shape: Velocity peaks at the top of the blade, then gradually decreases towards the bottom, resulting in a concentrated flow at the top.

• Concave Blade Shape: Similar to convex, velocity extremes occur at the perimeter, but the distribution is more uniform, with a smoother and more consistent flow gradient.

Figure 8.

Flow velocity in a horizontal direction: unstable flow in two points near the blade

The graph highlights how each blade shape affects flow dynamics, with the concave shape offering a more efficient, smooth velocity distribution while the vertical shape concentrates flow at the top.

Figure 9 shows the flow velocity distribution in turbulent flow for three blade shapes:

• Convex Blade Shape: Velocity dissipation is highest at the perimeter and end, leading to increased turbulence and reduced flow efficiency.

• Vertical Blade Shape: Similar to the convex shape, distribution is more concentrated at the edges; a smoother transition to lower velocities offers slightly better flow control.

• Concave Blade Shape: Dissipation is more evenly spread across the flow guide, reducing turbulence and suggesting more efficient flow control than convex and vertical shapes.

Figure 9.

Flow velocity in a horizontal direction during unstable flow

In summary, the concave shape offers smoother and more efficient flow control, while the convex shape shows higher turbulence and energy loss.

Figure 10 shows the flow behaviour for three blade shapes (convex, vertical, and concave) under minimal laminar flow conditions.

Figure 10.

Flow velocity in a horizontal direction during stable flow

Figure 11 illustrates the differential pressure across the cross-sections of the flow guide for three blade shapes:

• Convex Blade Shape: The pressure distribution shows significant differences at the perimeter, leading to higher pressure losses in the flow guide.

• Vertical Blade Shape: Similar to the convex shape, with pressure variations concentrated at the edges, but with a somewhat smoother pressure gradient.

• Concave Blade Shape: The concave shape provides a more uniform pressure distribution across the entire flow guide, resulting in more efficient flow control and lower pressure losses.

Figure 11.

Pressure deviation on two selected points on the blades wall

The concave blade is chosen due to its more consistent pressure distribution, which reduces turbulence and pressure losses and improves overall flow control efficiency.

• Convex Blade Shape: The convex blade causes a rapid velocity increase at the perimeter, followed by a sharp deceleration and stagnation at the end. This results in an abrupt change in flow, leading to potential inefficiencies and turbulence.

• Vertical Blade Shape: The vertical blade shows a velocity peak at the top, gradually decreasing towards the end. The transition is smoother than the convex shape, offering better flow stability, though still concentrated at the top.

• Concave Blade Shape: The concave shape maintains a more consistent velocity distribution, with a gradual increase at the top and a steady decrease at the end. This results in a smoother, more efficient flow with minimal turbulence.

In summary, the concave blade offers the most stable and efficient flow, while the convex shape shows sharp velocity changes and potential inefficiencies. The vertical shape provides a balance between the two.

Figure 12 shows the correlation of hydraulic pressure at two selected points in the medium for three blade shapes: convex, vertical, and concave.

• Convex Blade Shape: The pressure curve shows noticeable deviations, inconsistent pressure values and irregular flow behaviour.

• Vertical Blade Shape: Similar to the convex shape, but with less deviation, the pressure distribution is smoother but still shows some irregularities.

• Concave Blade Shape: The concave shape offers a smooth, consistent pressure curve with minimal deviations, ensuring more stable flow dynamics.

Figure 12.

Pressure dispersion at the top of the shoulder blade - vector prevalence

The concave blade is preferred for its uniform pressure distribution, which leads to more efficient flow with fewer deviations, ensuring better stability and control.

Figure 13 shows the velocity at the perimeter of the flow guide for three blade shapes: convex, vertical, and concave, in a stable flow condition.

• Convex Blade Shape: The velocity at the perimeter is uneven, with noticeable fluctuations indicating instability in the flow.

• Vertical Blade Shape: The velocity shows a more stable profile than the convex shape but still has some fluctuations at the perimeter.

• Concave Blade Shape: The concave blade provides the most stable and consistent velocity profile at the perimeter, with minimal fluctuations.

Figure 13.

Displaying speed on top of a blade

The concave blade is chosen for its smooth and stable velocity distribution at the perimeter, ensuring more efficient and consistent flow with reduced turbulence.

Figure 14 shows the differential pressure across the cross-sections of the flow guide for three blade shapes: convex, vertical, and concave, without iterative shifts.

• Convex Blade Shape: The pressure distribution is highly uneven, with significant pressure drops at the edges, leading to inefficiencies.

• Vertical Blade Shape: The pressure distribution is smoother than the convex shape but still shows some variations and pressure losses at the perimeter.

• Concave Blade Shape: The concave shape maintains a more uniform pressure distribution across the flow guide, minimising pressure losses.

Figure 14.

Differential pressure display on two allocated points on the edges of the blade

The concave blade is selected for its consistent and efficient pressure distribution, which reduces energy losses and improves overall flow stability.

Figure 15 shows the velocity at the perimeter of the flow guide and the surrounding flow dynamics for three blade shapes: convex, vertical, and concave.

• Convex Blade Shape: The velocity at the perimeter fluctuates significantly, indicating instability in the flow near the edges.

• Vertical Blade Shape: The velocity is more stable than the convex shape but still shows some fluctuation at the perimeter.

• Concave Blade Shape: The concave blade provides the most stable and uniform velocity at the perimeter, with minimal fluctuations.

Figure 15.

Display of laminar and turbulent flow to the perimeter of the blade perimeter

The concave blade is preferred for its consistent and stable velocity at the perimeter, ensuring more efficient and smoother flow dynamics with reduced turbulence.

Figure 16 illustrates shock and back pressure dissipation along the flow guide for three blade shapes: convex, vertical, and concave.

• Convex Blade Shape: The dissipation is concentrated at the perimeter and end of the blade, with significant pressure losses leading to reduced efficiency.

• Vertical Blade Shape: The dissipation is more evenly spread than in the convex shape but still shows some localised pressure drops at the edges.

• Concave Blade Shape: The concave shape exhibits the most uniform dissipation of shock and back pressure, leading to more efficient flow and reduced pressure losses.

Figure 16.

Display of pressure on the blade wall

The concave blade is chosen for its uniform dissipation of pressure, ensuring smoother flow, reduced turbulence, and higher overall efficiency.

Figure 17 shows the scalar velocity at the surface of the flow guide for three blade shapes: convex, vertical, and concave.

• Convex Blade Shape: The velocity at the surface shows significant variations, indicating irregular flow patterns and potential inefficiencies.

• Vertical Blade Shape: The velocity profile is smoother than the convex shape, but some irregularities remain, particularly near the edges.

• Concave Blade Shape: The concave blade provides the most consistent and uniform velocity profile across the surface, ensuring smoother and more stable flow.

Figure 17.

View of flow velocity vectors in correlation with pressure distribution at the top of the blade

The concave blade is selected for its uniform velocity distribution at the surface, which ensures efficient, stable flow with minimal turbulence and energy loss.

Figure 18 shows the flow velocity gradient throughout the flow guide for three blade shapes: convex, vertical, and concave.

• Convex Blade Shape: The velocity gradient is uneven, with sharp deviations and areas of rapid flow acceleration, leading to less efficient flow control.

• Vertical Blade Shape: The gradient is smoother than the convex shape but still shows some irregularities, leading to slight inefficiencies.

• Concave Blade Shape: The concave shape offers the most consistent flow velocity gradient across the device, ensuring more efficient and stable flow dynamics.

Figure 18.

Flow velocity on the blade's walls in two allocated points in the medium

The concave blade is chosen for its smooth, uniform velocity gradient, which reduces turbulence and provides better flow control and overall efficiency.

Figure 19 shows the combined effect of all three blade shapes (convex, vertical, and concave) on the flow dynamics within the device.

• Convex Blade Shape: The convex blades cause more localised turbulence and energy loss, with noticeable areas of disturbed flow near the edges.

• Vertical Blade Shape: The vertical blades result in less turbulence than the convex shape but still show some flow disturbances at the perimeter.

• Concave Blade Shape: The concave blades provide the most stable and consistent flow, with minimal turbulence and more efficient energy distribution across the entire device.

Figure 19.

Flow velocity on the mixer's walls

The concave blade is selected due to its distinct ability to minimise turbulence and energy loss, ensuring more efficient and stable flow control than convex and vertical shapes.

Figure 20 shows the flow velocity along the central axis of the flow guide for three blade shapes: convex, vertical, and concave.

• Convex Blade Shape: The velocity profile shows significant fluctuations, indicating instability along the axis and inefficiency in flow distribution.

• Vertical Blade Shape: The velocity profile is smoother than the convex shape but still shows some inconsistencies along the axis.

• Concave Blade Shape: The concave shape offers the most stable and uniform velocity profile along the axis, ensuring consistent and efficient flow throughout the guide.

Figure 20.

Flow velocity in the horizontal direction

The concave blade is chosen for its consistent velocity profile, which ensures smoother flow, reduced turbulence, and more efficient overall performance.

Figure 21 shows the projected pressures at two selected points in the flow medium for three blade shapes: convex, vertical, and concave.

• Convex Blade Shape: The pressure distribution shows significant pressure drops at the edges, with noticeable asymmetry in the flow.

• Vertical Blade Shape: The pressure distribution is more balanced than the convex shape, but there are still asymmetrical pressure drops at the edges.

• Concave Blade Shape: The concave shape provides the most even pressure distribution, with minimal pressure losses and a more symmetrical flow pattern.

Figure 21.

Differential pressures on the edges of the blade

The concave blade is preferred for its ability to provide a more uniform pressure distribution, leading to reduced turbulence and improved overall flow efficiency.

Figure 22 shows the pressure at the edges of the blades in a steady laminar flow for three blade shapes: convex, vertical, and concave.

• Convex Blade Shape: The pressure at the blade edges is uneven, showing significant pressure variation, which can cause flow disturbances.

• Vertical Blade Shape: The pressure is more uniform than in the convex shape but still shows some inconsistencies at the blade edges.

• Concave Blade Shape: The concave blade provides the most consistent pressure along the edges, ensuring stable and smooth flow.

Figure 22.

Differential pressures on two allocated points in the medium near the blade

The concave blade is chosen for its uniform pressure distribution, which reduces flow disturbances and ensures more efficient and stable flow control.

Figure 23 shows the relationship between velocity and pressure within the device for three blade shapes: convex, vertical, and concave.

• Convex Blade Shape: The velocity and pressure show significant fluctuations, indicating inefficiencies in flow control and increased turbulence.

• Vertical Blade Shape: The relationship between velocity and pressure is smoother than in the convex shape but still shows some irregularities.

• Concave Blade Shape: The concave shape provides the most consistent relationship between velocity and pressure, with minimal fluctuations and better flow stability.

Figure 23.

Flow velocity and pressure dependence in the tank

The concave blade is selected for its ability to maintain a stable and efficient balance between velocity and pressure, ensuring smooth and consistent flow with reduced turbulence.

Figure 24 illustrates the pressure drops within the device caused by free flow for three blade shapes: convex, vertical, and concave.

• Convex Blade Shape: The pressure drops are more pronounced, showing significant losses in efficiency due to turbulent flow near the edges.

• Vertical Blade Shape: The pressure drops are less severe than in the convex shape, but there are still notable losses, particularly at the edges.

• Concave Blade Shape: The concave blade shows the least pressure drop, with more consistent and efficient flow across the device.

Figure 24.

Pressure drops on the blade's walls

The concave blade is chosen for its ability to minimise pressure drops, ensuring more efficient flow with reduced turbulence and better overall performance.

Figure 25 shows the velocity vectors at the asymptotes of the flow guides for three blade shapes: convex, vertical, and concave.

• Convex Blade Shape: The vector speeds show higher variations, indicating more turbulence and inefficiency at the flow guide asymptotes.

• Vertical Blade Shape: The vector speeds are more consistent than the convex shape, but there are still some irregularities at the asymptotes.

• Concave Blade Shape: The concave shape provides the most uniform vector speed distribution, ensuring smoother flow and reduced turbulence.

Figure 25.

Flow velocity vectors on the walls of the mixer

The concave blade is preferred for its ability to maintain uniform vector speeds, leading to more efficient flow and minimised turbulence compared to the convex and vertical shapes.

Figure 26 illustrates the depth-wise dissipation of flow velocity for three blade shapes: convex, vertical, and concave.

• Convex Blade Shape: The velocity dissipation is uneven, with significant variations throughout the depth, leading to inefficiency and turbulence.

• Vertical Blade Shape: The dissipation is more uniform than the convex shape but still shows some irregularities through the depth of the flow.

• Concave Blade Shape: The concave blade shows the most consistent and smooth velocity dissipation, ensuring efficient flow throughout the entire depth.

Figure 26.

Vertical display of flow velocities on the edges of the blade

The concave blade is chosen for its uniform velocity dissipation, which reduces turbulence and ensures more efficient and stable flow control across the full depth of the device.

Based on the results from the graphs above, the concave blade shape emerges as the optimal choice for flow guidance. The concave shape consistently outperforms the convex and vertical shapes in multiple key aspects, including velocity distribution, pressure control, and flow efficiency.

1. Stable flow control: The concave shape provides a smoother and more consistent flow profile with reduced turbulence, as evidenced by more uniform velocity and pressure distributions across all graphs. This situation leads to better flow stability and efficiency than convex and vertical shapes, which exhibit more fluctuations and localised disturbances.

2. Minimised energy loss: The concave shape results in the least velocity dissipation and pressure drops, ensuring lower energy losses throughout the device. It is crucial for maintaining high efficiency in flow guidance systems.

3. Reduced turbulence: Unlike the convex and vertical shapes, which show localised turbulence, the concave blade reduces disturbances and maintains smoother flow dynamics, ensuring more effective control over the fluid.

4. Uniform performance: Across all simulations and measurements, the concave shape consistently provides better uniformity in pressure, velocity, and dissipation, making it more reliable and effective for efficient flow management.

Due to the system's complexity in purifying the liquid phase from the Sovjak pit, each device will be described separately.

The wastewater treatment device for the Sovjak pit was designed using IT, specifically simulation modelling. A model was developed for this work, and a system was designed based on experimentation.

Wastewater will be pumped from the pit and brought to the reception tank (PSB). It will carry out the procedure of primary deposition of the so-called equalisation. The pre-treated water will be directed to the hybrid package unit for water treatment (U2), where water conditioning processes (flocculation, coagulation, neutralisation) will occur. Depending on its quality at the unit outlet, the treated water can be recirculated back to the temporary tank PSB or the tank SB or absorption well. The sediment from wastewater treatment will be collected in temporary storage (PS2) and taken to the hydrocarbon treatment location.

Parameters of treated wastewater will be measured at the outlet using an online analyser for continuous, real-time monitoring of water quality. It provides immediate feedback on the treatment results, enabling operators to ensure that parameter values meet regulatory standards defined by national legislation and are safe for water discharge or further use. Common parameters measured by online analysers are pH, conductivity, Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), turbidity, Total Suspended Solids (TSS), and Dissolved Oxygen (DO). Measurements can also include concentrations of ammonia (NH₃), nitrates and nitrites, chlorine (free and total), Total Organic Carbon (TOC), and heavy metals - e.g., lead (Pb), mercury (Hg), arsenic (As), and cadmium (Cd).

The treated wastewater stream is discharged into the collection basin (SB), whose entrance is fitted with a GAC filter to reduce the slightest chance of the appearance of harmful substances. If the wastewater quality does not satisfy national regulatory conditions (compatible with EU legislation) for input into the SB, the stream is returned to the PSB tank for reprocessing.

The paper delineates the experimental results of the selection of blade shapes in the model. The experimentation involved concave, vertical, and convex blades, each tailored for distinct material qualities and mixing circumstances in mixing procedures. Concave blades are favoured over convex or vertical blades for mixing heavier, viscous substances and when shear pressures are required. Their inward curvature produces significant shear and turbulence, efficiently disaggregating clumps and facilitating a more comprehensive mixing of materials such as adhesives or viscous liquids. This design facilitates vigorous agitation, decreasing processing time while preserving productivity, especially in scenarios involving dense or difficult-to-mix substances. Consequently, concave blades were selected for the mixing procedures in treating wastewater from the Sovjak pit.

The research presented in this paper belongs to the interdisciplinary field of informatics and environmental protection. The environmental tests are transferred to a simulation model in different operating conditions, with the possibility of visualising and modelling the fundamental factors of the operation of a water purification system adaptable to changes and crises. Conceptualisation, parameterisation, and model development enable experimentation with no environmental consequences and optimise an adaptive water purification system that can be used in other locations and with different pollutant concentrations.