# SMART Computational Solutions for the Optimization of Selected Technology Processes as an Innovation and Progress in Improving Energy Efficiency of Smart Cities—A Case Study

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## Abstract

**:**

## 1. Introduction

- Process optimization.
- Enhanced biogas yield.
- Efficient on-site combined heat and power (CHP) generation.
- Co-digestion of sludge with food waste as a option to increase the biogas output.

- SMART Computational Solutions for the optimization of solid waste fuel production from separate combustible fractions—a polymorphic model of multi-threaded optimization of the production process of fuel components from combustible municipal waste fractions,
- Thread A—optimization of the light fraction production process with preset physicochemical, combustion and emission properties.
- Thread B—optimization of the production process of hybrid fuel components, by mixing the light fraction with fossil fuels, while fulfilling the objective function (Wd-calorific value) and constraints imposed on decision-making optimization tasks.

- SMART Wastewater Treatment Plant Computational Solutions-a model for optimizing the biological process of wastewater treatment using a multi-threaded hierarchical adaptive control algorithm, including:
- Thread A—a model for automatic control of the nitrification and denitrification process, while monitoring the value of total nitrogen in the treated wastewater,
- Thread B—a model for the optimization of internal and external recirculation processes of the bioreactor.

- SMART Waste Management Computational Solution,
- SMART Wastewater Treatment Plant Computational Solution.

## 2. Advanced Soft Computing Applications and Industrial Automation in Selected SMART Sectors

- IFL—Integrated Field Laboratories
- An advanced model of a virtualized laboratory space [12]. The IFL supporting the process of building and updating the knowledge base of an expert system (in the CASK layer).

- CASK—Cyberinfrastructure, Analytics, Simulation and Knowledge Discovery [12] (The expert system in the CASK layer includes the following functional specialized modules:
- EnviroLab.AdvancedProcessControl (an integrated package of diagnostic and optimization tools and advanced process control APC, predictive process control MPC),
- EnviroLab.EnvironmentalMonitoring (an advanced solution of HPC class, enabling simulation of pollution propagation and the optimization of technological process parameters in order to reduce the negative impact on the environment),
- EnviroLab.EnterpriseSolutions (an open and scalable platform based on .NET technology, using the latest IT technologies and modular plug-in solutions). The dedicated standard—EnviroLab.NEnviroT—provides SDK, API and many other tools—whereby it is possible to modify or expand applications, standard models etc.

- Cloud-GRID—technology [12]

- Preparation of a simulation model.
- Compilation of the project in the dedicated card containing analog/digital input/output interfaces.
- Development of a target hardware module, e.g., using the systems ASIC, FPGA, microcontrollers.

## 3. Materials and Methods

- Section 3.1—SMART Waste Management Computational Solution,
- Section 3.2—Computational Solution of SMART Wastewater Treatment Plant.

#### 3.1. SMART Waste Management Computational Solution

- Optimal calorific value (enthalpy of devaluation).
- Appropriate fractional, elemental composition, fuel properties (moisture content, combustible content, non-combustible content, volatile content, aggressive content.
- Suitable ash softening and melting temperature, flash point, combustion temperature), as well as relevant material properties (shape, porosity).

- Substances included in fuels have identified chemical properties and composition.
- The combustion process involving molded fuels is known.

#### 3.1.1. Algorithmic Model of Multi-Threaded Optimization of the Manufacturing Process of Fuel components

- optimization of the light fraction production process-consisting in separating (in the optical separator/s) from the heavy oversize fraction of waste stream- non-metallic components with high calorific value Wd, exceeding 38 MJ/kg in the case of PE using the advanced MPC predictive control algorithm (based on a Multi Class Vector Support Machine algorithm model) implemented in the optimizing control layer of the SCADA system,
- optimization of the production process of hybrid fuel components-consisting in mixing the light fraction (obtained as a result of the optimization of the optical separation process-thread A) with fossil fuels, while fulfilling the objective function, i.e., its maximizing $\underset{u\in S}{\mathrm{max}}{W}_{d}^{T}u$ while meeting the constraints imposed on decision variables of the optimization task, determined by the parameters of the realized technological processes. The optimization of the process is carried out by the modified simplex algorithm.

#### 3.1.1.1. Algorithmic Subsystem Model for the Optimization of the Light-Caloric Fraction Production Process—Thread A

_{d}), meeting at the same time the process and technological constraints imposed on the decision variables of the linear programming tasks. The class patterns are described by a set of features (texture, structure, spectroscopic spectrum), which allows the algorithm at a lower level of the control of the sorting process to carry out classification using the network model SVM (MC SVM) on the basis of a set of features assigned to predefined class patterns, represented by disjoint subsets of the cover, generated by the master algorithm, Greedy Set Cover, and finally to check if the identified component/fraction belongs to the class pattern by the incident matrix.

- NIR (near infrared)
- VIS (visible light)
- X-ray (high resolution x-ray)
- AAS (atomic spectrometry).

_{i}, d

_{i}), where x

_{i}is the input vector and d

_{i}is preset value (for two classes it reaches 1 for class 1 or −1 for class 2). Assuming a linear separability of both classes, the equation of hyperplane separating both classes can be written in the following form [19]

_{i}, d

_{i}) satisfies the above equation with an equal sign, then the vector x

_{i}= x

_{sv}forms the so-called support vector. Supporting vectors are those data points that are closest to the optimal hyperplane.

_{sv}.

_{sv}support vectors x

_{i}and the setpoints d

_{i}.

_{j}(x) for j = 1,2,..., K.

_{j}denotes the weights from φ

_{j}(x) to the output neuron. The vector W is K-dim and b is the weight of the polarization. The features of the process described by the functions φ

_{j}(x) have taken over the role of the individual variables x

_{j}.

_{i}in the following form:

_{i}x

_{j}) present in the formulated dual task is a scalar product of the vector function F $\phi \left(x\right)={\left[{\phi}_{1}\left(x\right),{\phi}_{2}\left(x\right),\dots ,{\phi}_{K}\left(x\right)\right]}^{T}$:

_{i}) and not on the activation function φ (x).

- 1
- $U\leftarrow X$
- 2
- $\xi \leftarrow \varnothing $
- 3
- while $U\ne \varnothing $
- 4
- to select $S\in F$, which is maximizing $\left|S\cap U\right|$
- 5
- $U\leftarrow U-S$
- 6
- $\xi \leftarrow \xi \cup \left\{S\right\}$
- 7
- return

_{d}from the oversize heavy fraction of waste stream, is represented by the matrix (column vector).

_{j}) of the components with stored indices of class patterns corresponding to the generated by the optimization algorithm Greedy Set Cover, subsets of the coverage of the set ${P}^{k}=\left({p}_{ij}\right)$ of the components p

_{ij}containing the i-th component contained in the waste stream directed to the optical separator, in the j-th class pattern.

_{d}, texture, spectroscopic spectra, structure.

_{d}and with the constraints on the decision variables of the optimization task.

Algorithm 1 MCSVM |

1: Input: Category N, input $D=\left\{{D}_{1},\text{}{D}_{2},\dots ,{D}_{N}\right\}$ for training samples; testing sample T. |

2: Output: Categories of T. |

3: Algorithm: |

4: // training section |

5: for n = 1 to N |

6: Positive Sample ← ${D}_{N}$, Negative Sample ← other samples except ${D}_{N}$ |

7: Store the data of $SV{M}_{n}$ classifier |

8: end for |

9: // testing section |

10: for n = 1 to N |

11: Use classifier $SV{M}_{n}$ to calculate the value of $f{\left(x\right)}_{n}$ |

12: end for |

13: Compare all $f{\left(x\right)}_{n}$, output the n corresponding to the maximum of $f{\left(x\right)}_{n}$ |

#### Algorithmic Model for the Optimization of the Production Process of Hybrid Fuel Components—Thread B

- There is one constrained solution (the objective function takes the smallest value at one vertex of the set of permissible solutions).
- There is an unrestricted solution (when the objective function can adopt any small value while not violating the constraints).
- There are infinitely many solutions (there are at least two vertices of the set of permissible solutions at which the function assumes the same minimum value).
- There is no solution, i.e., the set of permissible solutions is an empty set.

_{2}, O

_{2}, SO

_{2}, HCl, N

_{2}, H

_{2}O).

#### 3.2. SMART Wastewater Treatment Plant Computational Solution

- Automatic control of the nitrification and denitrification process, with simultaneous control of total nitrogen in the treated wastewater.
- Elimination of situations related to the overload of activated sludge chambers at the time when the inflow of the load of ammonium nitrogen in raw sewage was lower, and related to it disturbance of the denitrification process.
- Optimization of the internal and external recirculation processes of the bioreactor.
- Reduction of electricity consumption in the aeration and recirculation processes.

#### 3.2.1. Multi-Threaded Optimization of Electricity Consumption in the Biological Wastewater Treatment Process

_{4}) and nitrate (NO

_{3}) concentrations, whose permissible values are limited by legal regulations specifying the permissible parameters of nitrogen compounds in treated wastewater released into public waters.

_{4}) and nitrates (NO

_{3}) in the oxygen chamber is regulated by a multidimensional regulator. Oxygen concentration and the stream of recirculated wastewater are used as manipulated variables. Manipulated variables are used as external setpoints for lower level PID controllers. The biological reactor is divided into the anoxic part in which the denitrification process is carried out and the oxygen part in which the nitrification process is carried out.

_{4}) and the concentration of nitrates (NO

_{3}), and controls them through the concentration of oxygen and through the flow of the recirculating wastewater stream, by changing the pump efficiency effected by changing the rotation speed of the induction motor. The slave controller receives a signal from the MPC controller. The slave controller sends a signal requesting a change in the airflow, depending on the preset value of oxygen concentration (via the MPC controller). For this purpose, the subordinate controller determines the rotational speed of the blower motor.

#### Model of the Subsystem for Oxygen Concentration Control—Thread A

_{p}, controls:

_{o}, with the prediction of respiration value:

_{o}—oxygen concentration; k

_{La}—oxygen transfer function; Q

_{air}—inflow intensity of air to the oxygen chamber; S

_{(o, sat)}= 8.64 [g O

_{2}/m

^{3}]-oxygen saturation coefficient; K

_{o}= 0.2 [g/m

^{3}]—Monod constant for dissolved oxygen and R

_{r}—respiration.

_{La}was made dependent on the air supply rate Q

_{air}and it was adopted as the function:

^{3}/h].

^{3}/h].

#### Model of the Subsystem for Internal Recirculation Control—Thread B

_{p}—concentration of organic substances in the internal recirculation flow, mg BOD/L; C

_{mix}—concentration of organic compounds in the mixture of activated sludge and sewage, mg BOD/L; n

_{H}—internal recirculation coefficient, ${C}_{I}^{}$—concentration of organic compounds in the anaerobic bioreactor, mg BOD/L; ${t}_{I}$—duration of wastewater treatment in the anaerobic bioreactor, ${t}_{II}$—duration of wastewater treatment in the oxygen chamber; ${J}_{i}$—activated sludge indicator, cm

^{3}/g; R—recirculation ratio of activated sludge.

^{3}/day; ${Q}_{in}$—stream of inflowing sewage in m

^{3}/day; ${Q}_{r}$—recirculated wastewater stream in m

^{3}/day and ${Q}_{w}$—recirculated sludge stream from the secondary settling tank in m

^{3}/day.

_{z}, which is the product of the pump efficiency γ, induction motor γ

_{s}and frequency converter (inverter) ${\gamma}_{f}$:

#### 3.2.2. Simulation of the Operation of Biological Reactor-Classic Closed-Loop Control Versus MPC

_{4}), nitrate (NO

_{3}), dissolved oxygen in nitrification chambers (O

_{2}) and rotational speed of blowers (n).

## 4. Results and Discussion

#### 4.1. A Case Study—Energy Economics of Waste-Based Fuel Production Processes

- Optimization of the light fraction production process with preset physicochemical, combustion and emission properties (the Algorithm 1 MCSVM classifying waste fractions in the optical separator, based on the SVM network and the master algorithm greedy set cover).
- Optimization of the production process of hybrid fuel components, by mixing the light fraction with fossil fuels, while maximizing the objective function (W
_{d}—calorific value) and constraints imposed on the decision variables of the optimization tasks (modified simplex algorithm).

_{1}and U

_{2}with the following constraints on the decision variables: ${b}_{1}^{\mathrm{min}}\le {a}_{11}{u}_{1}+{a}_{12}{u}_{2}\le {b}_{1}^{\mathrm{max}}$, where ${a}_{11\text{}},{a}_{12}$ are sulphur mass fraction in components of the formed fuel and ${b}_{2}^{\mathrm{min}}\le {a}_{21}{u}_{1}+{a}_{22}{u}_{2}\le {b}_{2}^{\mathrm{max}}$, where ${a}_{21\text{}},{a}_{22}$ are chlorine mass fraction in components of the formed fuel. Symbols ${b}_{1}^{\mathrm{min}}$, ${b}_{1}^{\mathrm{max}}$ and ${b}_{2}^{\mathrm{min}}$, ${b}_{2}^{\mathrm{max}}$ denotes allowable values of sulphur and chlorine mass fractions in components of the formed fuel respectively (${b}_{1}^{\mathrm{min}}\ge 0,\text{}$${b}_{1}^{\mathrm{max}}\le 0.016\text{}\mathrm{kg}\text{}\mathrm{s}/\mathrm{kg}$ and ${b}_{2}^{\mathrm{min}}\ge 0,$${b}_{2}^{\mathrm{max}}\le 0.0024\text{}\mathrm{kg}\text{}\mathrm{cl}/\mathrm{kg}$. Optimum value of the objective function ${W}_{d}^{opt}$ was found within the range of 23.14 to max 24.31 $\mathrm{MJ}/\mathrm{kg}$.

#### 4.2. A Case Study—Energy Economics of Wastewater Treatment Plant Processes

^{3}/day. The biological wastewater treatment process is carried out in the UCT type flow bioreactor using activated sludge technology with fine-bubble compressed air aeration. The optimal aeration process of an aerobic chamber requires the supply of large amounts of electricity (to drive blowers) and accounts for over 45% of the annual electricity consumption in the entire wastewater treatment process. The second element of the installation that requires optimization of electricity consumption is the internal and external recirculation process, which accounts for over 10% of annual energy consumption.

- Maximum growth rates of autotrophs, heterotrophs and phosphorus accumulating heterotrophs.
- Decomposition rates of autotrophs, heterotrophs and phosphorus accumulating heterotrophs.
- Appropriate saturation constants and other coefficients, describing, e.g., the hydrolysis process, yields of growth of individual groups of microorganisms and other, with slightly less importance for the course of purification processes.

_{4}, N-NO

_{3}and total phosphorus as well as biomass concentration in the reactor.

- Growth rate of autotrophs: μ
_{A}= 0.69 - Growth rate of heterotrophs: μ
_{H}= 4.8 - Growth rate of phosphorus accumulating heterotrophs: μ
_{PH}= 3.9 - Autotroph decomposition rate: b
_{A}= 0.039 - Heterotroph decomposition rate: b
_{H}= 0.49 - Decomposition rate of phosphorus accumulating heterotrophs: b
_{PH}= 0.011

^{3}and 10 mg/dm

^{3}). The influence of the length of the Hp prediction horizon and the T prediction step on the control quality, measured by the RMSE error, was analyzed. The basic factor determining the choice of Hp was the dynamics of the analyzed object. The selection of the prediction step T is determined by the required control quality with the maximum reduction of computational complexity. This is particularly important in nonlinear systems. The RMSE (Root Mean Square Error) involving the follow up of oxygen concentration trajectory is given in Table 7. The said error was calculated from the relationship 48.

_{4}+ ammonium nitrogen, 30 mg/L for NO

_{3}- nitrate nitrogen and 1 mg/L for NO

_{2}- nitrite nitrogen, respectively.

_{4+}-N) and nitrate nitrogen (NO

_{3}-N) are presented in Figure 11.

_{4}load, but also a large load of organic pollutants. It follows that the load of activated sludge with an organic charge in the first part of the nitrification chamber is higher than in the second part of the nitrification chamber, and thus, in addition to the oxygen demand of Nitrosomonas bacteria (the first nitrification phase), very high oxygen demand is shown by heterotrophic bacteria that mineralize the organic contaminants. In the second part of the nitrification chamber, the demand for oxygen is definitely lower due to the fact that the greater part of the organic charge has been removed in the first part of the chamber. In addition, the demand for oxygen by nitrifying bacteria of the first phase of nitrification (oxidation of N-NH

_{4}to N-NO

_{2}) is as much as 75% of the total oxygen pool needed to achieve full nitrification, and for the second phase of nitrification (oxidation of N-NO

_{2}to N-NO

_{3}) oxygen by Nitrobacter only 25%, which is also associated with lower oxygen demand in the second part of the nitrification chamber.

_{3}-N in the outflow. Lowering the flow rate deteriorates the efficiency of the process. With the 50% recirculation, the nitrate nitrogen concentration is on the border of the permissible value of total nitrogen (<10 mg/L).

## 5. Conclusions

- Dedicated tool—Diagnostics and optimization of electricity consumption in network objects (pumps rooms, pumping stations, hydrophore rooms).
- Dedicated tool—Validation and reconstruction of process data (Sensor Data Validation and Reconstruction).
- Dedicated tool—Identification and diagnostics of errors (in real time) of measurement parameters in the hydraulic model and in the monitoring and control systems.
- Dedicated tool—Current (in real time) control of the performance characteristics and load profile of pump units.
- Dedicated tool—Diagnostics (in real time) of the operating point of pump units.
- Dedicated tool—Optimization (in real time) of the efficiency of pump units, including motors, inverters, regulation systems.
- Dedicated tool—Diagnostics (in real time) of error detection in the measuring paths of SCADA system.
- Dedicated tool—Diagnostics and optimization of the control quality indicator.
- Dedicated tool—Diagnostics and updating of pipeline characteristics.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

ICT | Information and communications technology |

SCADA | Supervisory Control And Data Acquisition |

FTC | Fault Tolerant Control |

HPC | High Performance Computing |

MPC | Model Predictive Control |

iRTDS | intelligent Diagnostic System in Real Time |

CEP | Complex Event Processing |

OPC UA | Open Platform Communications—Unified Architecture |

PLC | Programmable Logic Controller |

PADO | Performance Analysis Diagnostics & Optimization |

SVM | Support Vector Machine |

MCSVM | Multi-Class Support Vector Machine |

FPGA | Field Programmable Gate Arrays |

ASIC | Application-specific Integrated Circuit |

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**Figure 2.**Specialized platform of the virtual laboratory EnviroLab [own elaboration] [12].

**Figure 3.**Topology of the optimization control system [12].

**Figure 4.**Block diagram of the ballistic and optical separation processes separated from the technological line[own elaboration].

**Figure 7.**Simulation results for a variable raw sewage stream. The classic closed-loop control in red was marked in blue, using the MPC controller; (NH

_{4}) ammonia concentration, (NO

_{3}) nitrate concentration, (O

_{2}) dissolved oxygen concentration in nitrifying chambers, (n) blower speed, influent flow rate (feed) [own elaboration].

**Figure 8.**Block diagram of the process of municipal waste segregation with the production of the formed fuels [own elaboration].

**Figure 9.**Trajectories of oxygen concentration (reference and realized) in the oxygen chamber 1 [own elaboration].

**Figure 10.**Trajectories of oxygen concentration (reference and realized) in the oxygen chamber 2 [own elaboration].

**Figure 11.**Changes in the concentration of ammonium nitrogen (

**a**) and nitrate nitrogen (

**b**) in the both nitrifying chambers at different concentrations of dissolved oxygen [own elaboration].

Item | Parameter | Required Value |
---|---|---|

1. | Calorific value | ≥15 000 kJ/kg |

2. | Ash content | ≤20.0% |

3. | Total sulfur content | ≤2.0% |

4. | Chlorine content | ≤1.0% |

5. | Granulation | ≤40 mm |

6. | Moisture content | ≤30% |

7. | Average bulk density | 0.2 Mg/m^{3}–0.6 Mg/m^{3} |

8. | Scrap content | ≤0.5% |

Parameter | Permissible Value [ppm] |
---|---|

Total (As, Co, Ni, Sb, Pb, Cr, Cu, Mn, V) | 1000 |

**Table 3.**The results of the carried out tests (A and B)—result of application the classic control algorithm (Test A) and using the author’s algorithm presented in Section 3.1.1.1 (Test B).

Item | Fraction | Test A [Mg] | Test B [Mg] |
---|---|---|---|

1 | Undersize fraction | 3.7 (20.33%) | 1.79 (9.84%) |

2 | Oversize fraction | 2.42(13.30%) | 3.1 (17.03%) |

3 | RDF fuel | 10.74 (59.01%) | 13.3 (73.08%) |

4 | Loss | 1.34 (7.36%) | 0.01 (0.05%) |

Item | Parameter | Required Value |
---|---|---|

1. | Calorific value | 24.31 MJ/kg |

2. | Ash content | 8% |

3. | Total sulfur content | 1.4% |

4. | Chlorine content | 0.21% |

5. | Granulation | 30–38 mm |

6. | Moisture content | 24% |

7. | Average bulk density | 0.48 Mg/m^{3} |

8. | Scrap content | 0.32% |

Fuel Components | Mass Fractions in Components C1 and C2 (kg i/kg) | |||||||
---|---|---|---|---|---|---|---|---|

c | s | h | o | n | w | p | cl | |

C1 (dark coal) | 0.60350 | 0.00270 | 0.04100 | 0.08970 | 0.00010 | 0.07140 | 0.18420 | 0.00740 |

C2 (light-caloric fraction) | 0.50010 | 0.00920 | 0.06760 | 0.21860 | 0.13580 | 0.02000 | 0.04710 | 0.00160 |

Mass Fraction in Formed Fuel (kg i/kg) | ||||||||
---|---|---|---|---|---|---|---|---|

c | s | h | o | n | w | p | cl | |

Criterium | <0.016 | <0.0024 |

**Table 7.**RMSE prediction error with preset parameters Hp, T in oxygen chambers 1, 2 of the biological reactors.

MPC—Parameters | Root Mean Square Error RMSE | ||
---|---|---|---|

Prediction Horizon Hp | Time T [min] | Oxygen Chamber 1 | Oxygen Chamber 2 |

5 | 5 | 0.041 | 0.037 |

10 | 10 | 0.047 | 0.042 |

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**MDPI and ACS Style**

Gaska, K.; Generowicz, A.
SMART Computational Solutions for the Optimization of Selected Technology Processes as an Innovation and Progress in Improving Energy Efficiency of Smart Cities—A Case Study. *Energies* **2020**, *13*, 3338.
https://doi.org/10.3390/en13133338

**AMA Style**

Gaska K, Generowicz A.
SMART Computational Solutions for the Optimization of Selected Technology Processes as an Innovation and Progress in Improving Energy Efficiency of Smart Cities—A Case Study. *Energies*. 2020; 13(13):3338.
https://doi.org/10.3390/en13133338

**Chicago/Turabian Style**

Gaska, Krzysztof, and Agnieszka Generowicz.
2020. "SMART Computational Solutions for the Optimization of Selected Technology Processes as an Innovation and Progress in Improving Energy Efficiency of Smart Cities—A Case Study" *Energies* 13, no. 13: 3338.
https://doi.org/10.3390/en13133338