Multi-Objective Optimization for Ranking Waste Biomass Materials Based on Performance and Emission Parameters in a Pyrolysis Process—An AHP–TOPSIS Approach

Abstract

The current era of energy production from agricultural by-products comprises numerous criteria such as societal, economical, and environmental concerns, which is thought to be difficult, considering the complexities involved. Making the optimum choice among the various classes of organic waste substances with different physio-chemical characteristics based on their appropriateness for pyrolysis is made possible by a ranking system. By using a feasible model, which combines several attributes of decision-making processes, it is possible to select the ideal biomass feedstock from a small number of possibilities based on relevant traits that have an impact on the pyrolysis. In this study, a multi-criteria decision-making (MCDM) technique model based on the weight calculated from the analytical hierarchy process (AHP) tool has been applied to obtain a ranking of different types of agro-waste-derived biomass feedstock. The technique of order preference by similarity to ideal solution (TOPSIS) is used to examine the possibilities of using/utilizing locally available biomass. From this point of view, multi-criteria are explained to obtain yield maximum energy. The suggested approaches are supported by the experimental findings and exhibit a good correlation with one another. Six biomass alternatives and eight evaluation criteria are included in this study. Sawdust is the highest-ranking agricultural waste product with a closeness coefficient score of 0.9 out of the six biomass components that were chosen, followed by apple bagasse with 0.8. The hybrid approach model that has been built can be evaluated and validated for the ranking method using the Euclidian distance-based approximation. This study offers a unique perspective on decision-making, particularly concerning thermo-chemical conversion.

1. Introduction

Alternate forms of renewable energies have massive potential to replace traditional forms of energy resources extensively used in the modern world [1]. These renewable forms of energy are capable of providing electricity to their user at competitively lower rates with a lower carbon footprint on the environment. Since bio-energy is abundantly available and sustainable in nature, using it as a prime producer and source of energy to produce large-scale electricity seems a viable option [2]. Bio-energy ensures power security by offering a sustainable, cleaner, natural fuel option that is comparable to gasoline in terms of energy production. Biofuels are the most effective sustainable energy options for replacing fossil fuels as they perfectly fit the multi-dimensionality aspect of energy generation such as heat generation, transit, and the manufacturing of useful chemicals. The primary advantages of using biomass as a fuel source are its cost-effectiveness and 95% success rate [3]. In fact, such fuels add absolutely no contribution to the CO₂ emission rate in the environment [4]. Forest residue wastes, primarily waste woods, were once considered to be the most frequently used biomass feedstock for energy production. Biomass includes a variety of crop residues, sludges, and lignocellulosic materials possessing favorable characteristics for energy production [5]. Various conversion strategies utilize potent biomass materials to generate useful energy. Thermal conversion procedures [6,7] are a promising alternative for efficient bioconversion to biofuel among the various conversion strategies. Burning, decomposition, and gasifier are the basic thermal conversion methods. Burning is a straightforward method of converting fuel into heat and power. However, it is not eco-friendly, and its quality is poor, with ash control being another concern [8]. The main and most efficient way of bioconversion into methane (producer gas) is gasification; however, the plant investment costs should be considered. Because the gas created by this method cannot be maintained for a prolonged period, it must be used promptly [9].

In multiple criteria performance problems, conflicting targets and plans may be defined clearly as matrix development in the space containing range options [10]. Often decision-makers take judgment while considering the current energy policy matters, which might be hampered by maximizing profits [11]. A hybrid algorithm is viewed as a useful approach for enhancing highly responsive energy policy results and multiple performance decisions [12]. It is regarded as a viable method for generating improved flexible energy policy outcomes and decision-making. One such approach might be integrating the results obtained during experimentation with another technique that is called multi-criteria decision-making (MCDM). As a result, judgment-based decisions have broadened the scope of recent MCDM techniques to incorporate several types of techniques [13].

For underdeveloped and developing nations, pyrolysis offers the potential to construct a fresh and sustainable energy system for long-term use with a lower carbon impact [14]. The potential for burning discarded crop residues to produce energy is enormous in the field of renewable energy. Nevertheless, it becomes vital to pick and prioritize the residual agro-crop wastes depending on their pyrolysis viability from the large range of biomass materials [15]. There have not been any significant attempts to contemplate or rate various biomasses according to their potential for a pyrolysis process [16]. Prioritizing biomasses using scientific research on specific illustrations is not a feasible method because it consumes a significant amount of time and money. A feasible option, meanwhile, is to rank residual biomass materials according to their physio-chemical characteristics that affect the pyrolysis process [17]. A systematic process for choosing the best biomass for pyrolysis from a specific group of biomasses can be proposed, which is performed in this study. In this study, the viability of pyrolysis of several biomass feedstocks, called alternatives, is discussed and assessed in the form of their distinctive features (attributes).

Multiple-performance decision approaches are a sought-after versatile tool capable of dealing with multiple input constraints in several problems related to decision-making, thereby aiding decision-makers in addressing and solving the issue [18]. To add past data of people using such processes, numerous traditional MCDM techniques, such as the TOPSIS (technique for order preference by similarity to ideal solution), WDBA (weighted distance-based approximation), AHP (analytical hierarchy process), SAW (simple additive weight), ELECTR (ÉLimination Et Choix Traduisant la REalité) or “Elimination and choice Translating Reality”, PROMETHEE (preference ranking for organisation method for enrichment evaluation), etc., are used in multiple research articles to establish a feasible prioritization allocation among alternatives [19,20,21,22,23,24]. The initial criteria for choosing the optimal biomass as fuel, depend on multiple factors, such as biomass accessibility, pollution prevention, chemical activity, and system productivity. As a result, by considering numerous conditions and sub-conditions, an MCDM process makes a trade-off among various constraints of the biomass setup with the best possible performance characteristics as outcomes [25]. In the last 30 years, biomass energy has taken some progressive steps towards developing an alternative form of energy capable of replacing the existing forms of conventional energy.

Regarding problems associated with multiple performance decisions, MCDM techniques give more realistic and feasible results. Some popular methods used in prior studies have applied AHP, ANP, and TOPSIS in engineering applications [26]. Simultaneously, experts’ reviews are used to rate attributes for accurate and responsive answers. Multi-criteria decision making (MCDM) is a popular and a recognized tool for judging and ranking several frameworks of the different domains [27]. To deal with ambiguity in decision-based problems, this strategy is used to furnish effective and efficient results. The application of such MCDM techniques in equipment that impacts the environment does have several advantages, including the ability to integrate a multitude of different and often contrary fundamentals into characteristics, as well as creating a more flexible, fair, balanced, and effective review system with multiple options [28].

• Each criterion (attribute) in this hybrid approach has been given priority weight through the employment of the AHP approach. From a limited number of options or alternatives, solutions are found using the TOPSIS approach [29]. AHP is often considered one of the most potentially capable tools for weight assignment and is a widely used approach in the MCDM system. A major benefit of AHP is the pair-wise assessment. However, the traditional AHP technique does not match people’s cognitive tendencies [30]. To irradicate the above flaw, the hybridization of AHP is performed with TOPSIS, which AHP uses with the linguistic variables. This study offers a unique perspective of applying a hybrid model based on the ideas of multi-attribute assessment integrating AHP and TOPSIS, as little to no work has been reported indicating an immediate desire to grade the biomass residuals viable for the pyrolysis process based on some recognized attributes [31]. The following figure, Figure 1, shows the gap in research, which is still not explored in the previous literature to the authors’ knowledge. Some limitations of this study are that only eight control factors were taken into account for this experimental study, which could be further expanded.

To provide a comparative analysis, other models could be used along with AHP–TOPSIS, for the purpose of this experiment. The performance parameters of a single type of reactor were examined. Only a few performance traits were taken into account.

In this study, a novel effort is made to rank the biomasses according to those that can be successfully pyrolyzed. India is a vast hub of multiple agricultural waste options, which can be used at a local level to produce power. The biomass features that have been recognized are determined attributes that are used to evaluate various options or alternatives locally available state-wise [32]. By taking into account subjective weighting methods, it is feasible to determine the fundamental weights of the criteria. The study group’s extensive studies have provided insights into several physio-chemical characteristics of feedstock that seem to be relevant to the pyrolysis process. Lower heating value (LHV), moisture content (MC), carbon (C), ash content, hydrogen (H), oxygen (O), and nitrogen content (N), and the lower heating value of synthetic gas (LHV syngas) are significant factors that affect pyrolysis efficiency. The next section provides more information about choosing the right biomasses and identifying the relevant biomass characteristic features [33,34,35,36,37].

2. Materials and Method

This segment identifies the distinctive physio-chemical features of biomass that affect the biomass pyrolysis process. The formulation of the MCDM problem and the decision-making technique using a hybrid model made up of AHP and TOPSIS are discussed in subsequent sections.

2.1. Material Setup

Multiple biomass specimens were obtained from the state of Punjab, India, which is one of the top crop-producing areas. These specimens were further treated before employing them in the experimental setups for analysis. After being ground into a powder, the specimens were filtered to a range between 0.5 and 1 mm in particle size. In order to lower the specimens’ moisture content, they were dehydrated in a furnace at 150 °C for 30 min. A thermally insulated furnace was used inside a sealed room for this test. The attained waste biomass materials were in accordance with various standards ascertained by ASTM codes. The biomass waste used in the process is shown in detail in Figure 2.

The specimens’ lignocellulose biomass composition was examined by using sodium chlorides and acetic acid, which predict the cellulose and hemicellulose content. Furthermore, the alkaline cellulose content was identified by applying a two-step sulphuric acid degradation method. The mixture was cleaned and purified with cool liquid at the end, and the residual waste was identified.

By skipping the same study that was previously completed by selecting low-ranking waste biomass, as was the case for a few periods, the current approach can enhance our study in addition to discovering promising biomass substances. This study’s goal is to explain the importance and results of endogenous constraints that could raise the intensity. In order to carry out the indicated second step and conduct an objective assessment, this study defines a model as the fundamental tool. Additionally, we want to learn more about the constraint/variable-based performance matrix assessment and how it relates to how substitutes behave in experiments. A practical study was carried out for additional studies based on the recommendations made by the analytical assessment.

2.2. Waste Agro-Products Characterisation

The qualities of the biomass have the biggest impact on its pyrolysis reaction viability. The reactions and experimentations drawn from the extensive tests conducted in the aforementioned process are explained in this section. To identify the different biomass attributes, characterization experiments were carried out. The main biomass properties that affect the viability of gasification were identified as oxygen content (O), moisture content (MC), nitrogen content (N), ash content (ash), fixed carbon content (C), hydrogen (H), lower heating value (LHV) and LHV_syngas. The feedstock chosen for the pyrolysis reaction is primarily responsible for the energy and exergy content produced. The heating value of the material being examined for pyrolysis is, therefore, crucial. The feedstock’s lower heating value (LHV) is taken into account when analyzing it. The heating value is commonly affected by the elemental (carbon, hydrogen, and oxygen) parts of the biomass. The moisture part of the biomass feedstock mainly influences the pyrolysis feasibility. Thermal efficiency is inversely proportional to high moisture (MC). The removal of moisture content consumes a large portion of the heat produced; therefore, this energy is not accessible for the reduction reactions or for changing heat energy into chemical energy in the vapor. As a consequence, materials with high levels of moisture typically have lower heating values. The power potential of biomass is positively impacted by the fixed carbon (C) of the material. The biomass’s ash and fixed carbon (C) concentration indicate how easily it may be pyrolyzed. Ash, an inorganic material, is the waste product left over after pyrolysis. The amount of leftover stuff would have been greater if there was more ash. The amount of ash in the biomass feedstock proportionally affects how much power is accessible in the fuel. Therefore, reduced ash content biomass residuals are preferred. The following methods and instruments were used for the evaluation of biomass properties as shown below:

• Proximate analysis: determines the amounts of moisture, ash, volatile matter, and fixed carbon in the biomass sample;
• Ultimate analysis: determines the elemental composition of the biomass sample, including carbon, hydrogen, nitrogen, sulphur, and others;
• Calorimetry: measures the heating value or energy content of the biomass sample, using instruments such as bomb calorimeters and differential scanning calorimeters;
• X-ray diffraction (XRD): analyzes the crystalline structure of the biomass sample, providing information about the types of cellulose and hemicellulose present.

These methods and instruments are used to determine the composition and properties of biomass, which is important for understanding its potential applications and determining its suitability for use as a pyrolysis fuel for powerplants. Six alternative biomass waste agricultural feedstocks are commonly available in north India and are evaluated based on these characteristics. These include solid waste, rice husk, sawdust, sugarcane bagasse, apple bagasse, and manure. The cellulose, hemicellulose, and lignin contents of various solid waste materials are as follows. Rice husk: cellulose—20–35%, hemicellulose—15–25%, and lignin—10–20%; sawdust: cellulose—40–50%, hemicellulose—25–35%, and lignin—20–30%; sugar cane bagasse: cellulose—40–60%, hemicellulose—20–30%, and lignin—10–20%; apple bagasse: cellulose—35–50%, hemicellulose—25–35%, and lignin—10–20%; and manure: cellulose—less than 4%, hemicellulose—less than 4.5%, and lignin—less than 3.9%. The error analysis was executed using the submission of squares for all discrete equipment’s measured during the analysis.

The overall percentage of uncertainty (U) = square root of [(error level in C value)² + (error level in H value)² + (error level in O levels)² + (error level in N value)² + (error level in LHV value)² + (error level in ash value)² ]^1/2

The overall percentage uncertainty = square root of [(1.0)² + (1.0)² + (1.0)² + (1.0)² + (1.5)² + (0.1)² + (1.0)² + (0.2)²+ (0.1)²]^1/2

The overall percentage of uncertainty = ± 2.70%

2.3. Advantages of Proposed Work

The advantages of pyrolysis for the selection of biomass materials using multi-criteria decision making (MCDM) techniques are: increased energy efficiency: the pyrolysis process results in the conversion of biomass into useful fuels, such as bio-oil and biochar, which can be used as energy sources. This process can improve the energy efficiency of biomass utilization; reduced greenhouse gas emissions: pyrolysis reduces greenhouse gas emissions compared to traditional combustion processes, as it results in the production of biochar, which can be used for carbon sequestration; Improved economic viability: the production of bio-oil and biochar can generate revenue, making the pyrolysis process economically viable. The use of MCDM techniques can also help identify the most economically viable biomass materials for pyrolysis; reduced waste management problems: by converting biomass waste into useful fuels, the pyrolysis process can help reduce waste management problems associated with traditional waste disposal methods; increased sustainability: the pyrolysis process can contribute to the development of a more sustainable energy system by reducing dependence on finite fossil fuels and increasing the utilization of renewable biomass resources.

3. Methodology

Attribute weights or attribute scores are crucial fragments of information for MCDM-involving processes. While criterion values reflect the traits, characteristics, and outputs of alternatives, characteristic weights are used to determine the importance of features in a specified process.

The steps that make up the planned analysis for the pyrolysis of waste biomass materials are as follows:

• Criteria for choosing biomass feedstock (alternatives);
• Weightage calculation using AHP;
• Final decision matrix creation;
• Ranking of the substitutes choosing the best waste biomass feedstock;
• TOPSIS-based ranked matrix comparison with conventional matrix.

The main goal of this study is to develop a multi-criteria decision-making (MCDM) strategy to prioritize available waste biomasses feedstock easily found in the Indian subcontinent according to their pyrolysis viability. After the objective has been identified, suitable alternatives (various waste biomasses) and their properties (biomass traits) are classified and identified. The next stage is to put into practice an appropriate method for the selected biomass features’ priority weights. The rankings achieved for the biomass feedstock are again obtained using the appropriate TOPSIS approach. The next subsections demonstrate how to prioritize weights for their qualities and rank the choices.

3.1. AHP Methodology

Nowadays, a significant proportion of biomass waste is not processed and is disposed of in the trash or is burned outdoors. Biomass residues in high amounts are gathered from diverse areas, preferably from agricultural states, and are heated up in a reactor to produce power. Environments are seriously affected by inappropriate biomass feedstocks and improper pre-processing. Inadequate equipment and unavailability of labor reduce process effectiveness and harm process machinery. The grade of the feedstock also affects the physio-chemical properties of the generated fuel. An MCDM technique is employed for the first time to examine the best biomass material under different operating conditions so as to identify the best biomass material based on energy and exergy outputs.

It could also be used for both types of problems, i.e., theoretical and practical datasets. The hybrid technique is unique in investigating the ranking substitutes by incorporating a modern procedure that furnishes the best possible results with utmost reliability. This aids in investigating the regularity and consistency of the working procedure.

The following is a quick overview of AHP steps:

Step 1: Firstly, it is important to recognize the goals of the system, which needs to be attained with minimum possible constraints.

Recognizing the essential constraints of the problem is accomplished by varying them on the basis of computable distinctive feature whose main requirement depends on real-time experience. The recommendations should come from seasoned experts capable of providing a possible explanation for each possible choice they select.

Step 2: This step particularly, takes into consideration a detailed assessment, which is considered essential, and is further divided into two segments: (i) Amongst the criterions of any specific consideration; (ii) Amid various alternative results capable of fulfilling every pre-defined problem assumptions.

Specialists in the above specified domain aid in developing a balanced set of matrices that analyze multiple criterions on a varying scale fluctuating between 1 and 5.

Total attained number of interconnected vectors are estimated with the aid of the formula (nxn), where n stipulates the amount of criterions selected for the problem.

Step 3: Consider and assume Xij as the variable that indicates the ith factor’s precedence ranking in contrast to the jth factor. Therefore, we can write the final equation in the given form, Xji = 1/Xij.

Step 4: Furthermore, in this step a standardized matrix is prepared and formed for the above model analysis by the aid of subsequent sub-steps:

• Calculate the overall sum of values from every possible column present in matrix;
• Remove and subtract the sum of the resultant values from each column in the final matrix form;
• Finally, estimate the adjacent weights, analyzing and summing up the number of rows.

Step 5: With the application of the equation given below, the model finally obtains its assessment matrix, highest evaluation result, also known as the criterion index (CI)

$$CI=\frac{\lambda \mathit{max}-n}{n-1}$$

(1)

The eigenvector of the final estimated matrix is considered zenith, with the sum of criterions being equal to n.

Step 6: Earlier, the incorporation of the above equation yielded the CI value, which is used to estimate the consistency ratio (CR) of each criterion, as given in the below equation; here, RI is interpreted as a random index

$$CR=\frac{CI}{RI}$$

(2)

3.2. TOPSIS Methodology

The TOPSIS approach is further employed to estimate the ranking after the weights of every criterion taken into account are determined. Hwang and Yoon prescribed the TOPSIS method, capable of furnishing results more easily and quickly. Over the years, it has developed as one of the most effective approaches for comparing options. The TOPSIS approach bases its judgment upon the distance attained between the perfect and imperfect results. The criteria beneficial to the system are maximized, while non-beneficial criteria are minimized for a suitable and ideal response. Such an approach is straightforward to understand with a simple mathematical notation, while comparing multiple alternatives with each other. This approach is well-suited to a variety of applications, especially in engineering problems.

The weightage results determined by the AHP method are further transferred to the TOPSIS procedure, which further grades different outcomes based on the criterions’ impact on the entire system or model. This further enhances the performance of the setup, since prime outcomes are identified and essential parameters are given more weightage than usual. The combined modelling process is achieved from the given steps explained below:

Step 1: Primary consideration comprises of collecting essential reviews from selected experts, which result in developing a matrix comprising linguistic numbers, grading the criterions on the basis of the outcomes of the system. The linguistic variables provided to experts range from (1) to (5), which suggests tremendously low, low, regular, high, and tremendously high from the specialists. Equations (3)–(14) were used from an earlier study [38].

Step 2: Converting available data values from experts (linguistic figures) into possible understandable estimations.

X_abN = (l_abN) Where, a = 1, 2, 3, …, m; b = 1, 2, 3, …, n, where,

$$a=\min \left\{l_{abN}\right\}, b=\frac{1}{N}{{\displaystyle \sum }}_{N=1}^N{P}_{abN }, c=\max \left(u_{abN}\right)$$

(3)

Step 3: Estimating the final result evaluations comprising collective weights.

$$B={\left[P_{ij}\right]}_{mxn}$$

(4)

Here, i = 1, 2, 3, 4, …, m; j = 1, 2, 3, 4, …, n

$$P_{ij}=\left(\frac{a_{ij}}{c_j^{*}}\right);c_j^{*}=\max c_{ij}$$

(5)

$$P_{ij}=\left(\frac{a_j^{-}}{c_{ij}}\right);a_j^{*}=\min a_{ij}$$

(6)

Step 4: Normalizing the general resultant matrix.

$$V={\left[v_{ij}\right]}_{mxn}where,i=1,2,3,\dots ,m;j=1,2,3,\dots ,n$$

(7)

$$Here,v_{ij}=p_{ij}\left(\times \right)w_j$$

(8)

Step 5: Calculating the homogenous weightage attained matrix.

$$A^{+}=\left\{v_1^{+},\dots \dots ,v_n^{+}\right\}$$

(9)

where,

$$\begin{array}{c}{v}_j^{+}=\left\{\max \left(v_{ij}\right) IFj\in J;\min v_{ij}IFj\in J^{\prime }\right\},j=1,2,3,\dots ,n\\ A^{-}=\left\{v_1^{-},\dots \dots ,v_n^{-}\right\}\end{array}$$

(10)

where,

$$v_j^{-}=\left\{\max \left(v_{ij}\right) IFj\in J;\min v_{ij}IFj\in J\text{′}\right\},j=1,2,3,\dots ,n$$

(11)

Step 6: Furnishing the optimum results, which might be either positive or negative.

$$d_i^{+}={\left\{{{\displaystyle \sum }}_{j=1}^n\left(v_{ij}-v_{ij}^{+}\right)\right\}}^{1/2};i=1,2,\dots ,m$$

(12)

$$d_i^{-}={\left\{{{\displaystyle \sum }}_{j=1}^n\left(v_{ij}-v_{ij}^{-}\right)\right\}}^{1/2};i=1,2,\dots ,m$$

(13)

Step 7: Figuring out the variances among the actual data collected from the ideal, both positive and negative.

$$C{C}_i=\frac{d_i^{-}}{d_i^{-}+d_i^{+}};i=1,2,\dots ,n$$

(14)

Step 8: Approximating the final essential value of closeness coefficient (CC) statistics and further estimate ranking is achieved, beginning with the value having a zenith CC value, thereby manifesting it with the uppermost rank. Then, again, the rank lowers with the declining CC value.

3.3. Alternatives and Criterions

Here, six (n = 6) alternative biomass residuals/feedstock, saw dust, apple bagasse, rice husk, solid waste, sugarcane bagasse, and manure, are used in this study. Here, each is ranked based on eight criteria-selection characteristic properties—fixed carbon (C), hydrogen (H), the low heating value of volatile matter (LHV), nitrogen (N), moisture content (MC), ash content(ash), and low heating value syngas (LHV_syngas). The decision-making approach for the pyrolysis/gasification process includes 6 alternatives (n = 6) and 8 attribute/selection criteria (m = 8). Every alternative has to be analyzed in the form of 8 attributes/selection criteria, which depend on biomass feedstock properties affected by the energy contents of producer gas produced/unit mass (kg) of dry air biomass. The decision matrix created depicts all attributes for the applied outputs, as shown in

Table 1. Decision matrix for all waste biomass products.

Property	Inputs
	C	H	O	N	LHV	Moisture Content	Ash	LHVsyn
Units	Beneficial	Non-Beneficial	Beneficial	Non-Beneficial	Beneficial	Non-Beneficial	Non-Beneficial	Beneficial
	Wt%	Wt%	Wt%	Wt%	kJ/kg	Wt%	Wt%	kJ/kg
Solid Waste	48.47	6.431	37.21	2.51	23,770	14	4.75	8645
Rice Husk	34.5	4.66	34.08	0.56	21,833	14	21.09	8882
Sugarcane Bagasse	44.84	6.65	40.94	2.56	16,454	19	2.56	8949
Apple Bagasse	50.68	7.57	34.96	1.66	20,035	18	2.13	9053
Manure	47.69	6.17	32.87	4.94	23,943	47	0.85	8521
Saw Dust	49.66	4.94	38.80	0.44	22,530	28	1.14	8854

.

Low heating value (LHV), moisture content (MC), fixed carbon(C), hydrogen (H) ash content (Ash), oxygen(O), fixed nitrogen(N) content, moisture content (MC) and low heating value of syngas (LHV_syngas) qualities are eight identified biomass feedstock characteristics (attributes) that are prioritized using the AHP process. Following the procedures outlined, TOPSIS is then used to calculate and rank locally identifiable waste biomass, including solid wastes, rice husk, apple bagasse, sugarcane bagasse, sawdust, and manure. The output is verified using other well-known MCDM methods.

Henceforth, the following are the primary benefits accomplished by the application of this strategy in engineering problems:

• A concept that is simple, reasonable, and easy to apply;
• A sensible and straightforward rationale that depicts and reciprocates human decision;
• An analysis that is efficient in terms of computing;
• An easy-to-comprehend numerical score that indicates the optimum best and worst choice;
• The ability to assess actual data in a basic mathematical manner for every option;
• The possibility of projection and prediction.

4. Result and Discussion

Often several problems have conflicting criteria pertaining to performance while selecting an ideal alternative. The application of such methods is quite beneficial for determining good options for certain problems without compromising the performance of the system. The importance of MCDM approaches in many industrial applications was validated by the literature sections listed above. It is a comparatively recent concept to use MCDM to determine acceptable biomass material for optimal pyro-analysis. The transformation techniques, reaction mechanisms, enzyme addition, and biomass fuel improvements are all popular topics in pyro study [17]. However, the purpose of this research is the selection and implementation of the best alternatives to biomass, with the aid of the MCDM approach. In this part, the investigated outcomes and discussions based on the application of the MCDM approach in choosing the pyrolysis viability of various biomass feedstocks are given. Each biomass feedstock characteristic property’s (criterion) weighted preferences are established by the AHP. The pairwise/bilateral comparison of the decision-making-relevant biomass feedstock characteristics yields a comparison matrix. Based on the information gathered from the authors’ experimental studies into biomass pyrolysis, they conducted a pair-wise comparison analysis.

4.1. Weightage Assignment

Because of the convenience and capacity to take into account multiple numbers of possibilities and criteria throughout the decision-making stage, TOPSIS and AHP are convenient techniques for such complex problems. The weights given to the evaluation criteria that affect the rank stability can be derived using different MCDM techniques. In this assessment, the weights were determined using the AHP approach, while prioritization was achieved by the TOPSIS method. The reliability of the pairwise comparison matrix can be evaluated by computing the consistency ratio, which is the ratio of the consistency index to a random index that was found to be in accordance with the set standards.

Moreover, the criterions selected for the modelling process are weighed and valued, while incorporating prioritizations as depicted in Figure 3.

The global priority weightages attained for each parameter along with their rankings are demonstrated in

Table 2. Ranking priority of attributes/criteria based on weightage.

Attributes	Weight	Importance Criteria Rank
C	20.8%	2
H	14.2%	4
O	6%	7
N	22.9%	1
LHV	7.1%	5
Moisture Content	6.2%	6
Ash	6.2%	6
LHV of Syngas	16.3%	3

.

This table, Table 2, contains the value of the random index after analysis. The comparison matrix in AHP is converted to a pair comparison matrix for accurate comparison among alternative criteria to attain consistency in final weights. From the AHP analysis, it is found that the maximum weight or importance are found in the nitrogen content attribute (23%) of the waste biomass material, while oxygen content is the lowest (6%). From the analysis, it can be inferred that experts have considered nitrogen content to be the most significant attribute, while oxygen content is the least desirable by experts. The final attained comparison matrix is reliable as per the weights attained in the process. The final weightage calculated through AHP and applied in the next step for TOPSIS is displayed in Figure 4.

4.2. Ranking and Prioritization

By incorporating the above weights and ranking them, a prioritization model is developed, which identifies and classifies the essential parameters while considering certain outcomes of the system. The performance model was developed with the aid of the AHP–TOPSIS method, which transforms a complex problem into an easily explained problem with a single performance score indicating the prioritization of outcomes. The AHP method furnished above was employed to estimate essential weights for multiple criterions, which play a vital role in varying the performance of the system. The weights attained were further transferred into the TOPSIS model, which uses them to periodize some special parameters. This is further used to compute a single performance score for the multiple waste biomasses ranking them from finest to foulest. In the meantime, the elements of the performance characteristics were fairly unrelated; henceforth, these were normalized to convert them into a similar range.

Further, overall performance scores were calculated based on the weights derived from the AHP process, and the scores of all biomass materials were ranked accordingly from the highest score at the top to the lowest at the bottom. The values were obtained after normalizing the weights of the matrix.

The summation of the positive and negative distances from the mean solution was determined in the next phase.

To determine both values, we calculated the final assessment score to obtain the specified biomass product prioritization using the closeness coefficient values. The product with the highest value was ranked first, followed by the second nest for the attained scores of other biomass materials. The relative closeness (Pi score) of each locally identified biomass (alternative) is represented in Figure 5. It is found that saw dust achieves the highest value of relative closeness (Pi score nearly equal to 0.9) making it the best alternative among the biomasses considered in terms of energy content per unit mass (kg) of dried-air biomass.

Manure found has the least relative closeness (Pi scores nearly equal to 0.3), and is ranked as the biomass having the lowest viability for pyrolysis.

The biomass products were prioritized by the TOPSIS approach as saw dust > apple bagasse > rice husk > solid waste > sugarcane bagasse > manure. Here, saw dust is identified and placed in the top bracket followed by apple bagasse in the second position, under this approach.

4.3. Validation of the AHP–TOPSIS Model

The results attained after the weight assignment and prioritization process through the AHP–TOPSIS hybrid approach model are validated by comparing with certain existing approaches on waste biomass selection. Additionally, the current modelling aspect is justified by the Euclidean distance-based approximation (EDBA) method established. Here we compared the achieved Euclidean distance for all biomass alternatives. These techniques were used to evaluate the viability of burning already chosen among the biomasses for the pyrolysis process. After experimental exergy analysis, the above results were again validated based on the same reactor settings for all feedstock pyrolysis analysis presented in Figure 6. Saw dust showed minimum losses in input energy as compared to its other counterparts, while manure presented high exergy losses.

In order to authenticate and validify the choice of selecting the integrated process of AHP–TOPSIS, preceding biomass-related studies are stated to establish the feasibility and reliability of this research. In accordance with the current study, the prior literature also follows the same pattern, and almost all major studies established saw dust as a prime biomass feedstock (except for one exception) compared to other potential waste agro-products.

Table 3. Comparison of previous available models with the proposed model.

References	Model	Best Feedstock
Mojaver et al. (2020) [39]	VIKOR-ENTROPY	Eucalyptus
Dhanalakshmi et al. (2021) [40]	MOORA	Saw Dust
Liu et al. (2018) [41]	Experimental	Pine Saw Dust
Current study	AHP-TOPSIS	Saw Dust

shows the different studies undertaken to identify the best biomass as feedstocks.

4.4. Discussion

Prior to contemporary work, it was extremely complex to contemplate which parameters have a stronger influence over the biomass pyrolysis process. The identification and prioritization of attributes was another area where experts required a proper logical and efficient decision. Biomass selection from waste agricultural product estimation is a complex problem in pyrolysis-based equipment, which might be solved with the aid of hybrid MCDM procedures. After being heated in a furnace, the majority of the biomasses’ moisture percentage dropped substantially. The biomass made from sawdust had a higher carbon and hydrogen concentration than the other raw biomass. Moreover, the nitrogen concentrations were on the lower side. Henceforth, it is essential to consider the nitrogen content of waste biomass since it is significantly lower compared to other biomass feedstocks available. The biomass nitrogen concentration contributes to greater nitrogen oxide emissions being produced during the pyrolysis reaction. Yet, the amount of nitrogen in the biomass varies on the feedstock and is difficult to reduce. So, there is an advantage in using sawdust as biomass with lower emissions.

The biomass ash content also plays a vital role in pyrolysis reaction setting conditions. A higher percentage of ash content in fuel negatively impacts the burning process. Additionally, higher ash present at sight is difficult to handle and dispose of. In comparing the saw dust ash percentage generated with other biomass feedstock is slightly on the lower level (around 1.14 wt%), as compared to rice husk, which is as high as 21.19 wt%. This is another crucial additional advantage that falls under the sawdust category, thereby validating the design and results of this research.

Another important criterion that plays a vital role in the combustion process is the calorific value of biomass feedstock. Improper calorific value puts undue pressure on the reactor, thereby generating high smoke due to the improper burning of biomass. The sugarcane bagasse bio-oil was found to have an appreciable calorific value of 22,530 KJ/kg as compared to its counterparts. The use of low-grade biomass for furnace heating possesses serious problems for reactor and process reactions. Higher heating value is desirable, at least 50% of gasoline calorific value. This value is acceptable for a waste product generated through agricultural activities. However, the effectiveness during combustion will be decreased by the existence of moisture.

The output of the thermo-chemical process relents is influenced by biomass properties like cell wall content, mineral composition, and water content. Enhancing lignocellulose content and lowering ash and wetness levels can improve fuel sources for thermo-chemical processes. For a spatial analysis of a thermo-chemical conversion process, the conclusion and related organic matter characteristics like hydrogen and carbon, oxygen and carbon, and the calorific value, are more significant. The utilization of biomass for worldwide power generation would definitely benefit from an understanding of these biomass properties, which would also advance our knowledge of how flora modifies its chemical makeup in response to their surroundings. Modern energy conversion strategies that depend on a hit-and-trial method and error are frequently seen as useful.

Therefore, choosing the optimum biomasses necessitates a planned and successful strategy. The above analysis makes an attempt to determine the variables that influence the choice of feedstock for pyrolysis to produce the maximum energy in the pyrolysis process. By modifying the weighing of the biomass characteristics and expected outputs, the analysis can also be extended to include other thermo-chemical activities like co-firing with coal, gasification, liquefied, and partial oxidation.

5. Conclusions, Limitations, and Future Scope

Conclusions

The top biomass composites offered are investigated in this study by using a fuzzy-based MCDM method that is capable of assessing several criteria. There are several options involving several parameters that were compared for a pyrolysis procedure. The rating of biomass materials is investigated using fuzzy TOPSIS and AHP approaches. In this investigation, a platform was built for future researchers to choose biomass material on various parameters graded by MCDM techniques. Appreciable results have been drawn from the analysis, which is in agreement with the suggested methods also validated by previous research. The findings offer some encouragement for using multiple criteria decision-making (MCDM) tools for identifying and selecting probable biomass feedstock. These techniques placed saw dust and apple bagasse as first and second ranked biomass fuels, giving the highest bios after pyrolysis under the same input conditions.

This type of research has seldomly been considered before, probably due to the involvement of some constraints. Because of numerous restrictions, such as resource restrictions, studies often have boundaries. Several other biomasses can be explored and are popular in other countries.

• The analyzed results were put to the test practically in a live reactor, and the final experimental results were in agreement with the MCDM results, thereby self-assuring the application of the above-integrated technique in other power-based industries in the near future. In order to make important additions to the field of knowledge, researchers should plan and carry out investigations to examine previously seen and unseen processes in response to the study’s restrictions;
• The end products produced from sawdust, which has an appreciable calorific value that is slightly acidic, can be explored;
• Additionally, biomass material contains a variety of chemical constituents that can be exploited for economic uses, including liquors, phenolic, acids, alkyl, olefins, and aromatic hydrocarbons;
• Other appropriate biomass feedstock can be explored for other processes, such as decomposition, incineration, hydrolysis, and partial oxidation, so as to identify top-notch feedstock by applying similar MCDM approaches using multiple techniques that facilitate an easier and more accurate decision-making process.