Introduction

One of the main goals targeted at the 26th Conference of the Parties (COP26) is to reach net zero carbon emission by 2050 by ending deforestation, reducing methane emissions, and avoiding using coal as the energy provider. Coal is the second-largest energy source, accounting for 30% of the energy consumption globally to generate electricity (Adedoyin et al. 2020). The consumption of coal is led by China (50.5%), India (11.3%), and the USA (8.5%) (Chien et al. 2022). The large-scale usage of coal in emerging countries like China and India is because of their rich coal reserves, advanced exploration, mining, and utilization technologies, as well as the promotion of industrialization (Duan and Luo 2022). The growth rate of coal for energy consumption has slowed down significantly based on the review of world energy statistics conducted by British Petroleum in 2019. However, coal consumption, especially in China and India, is still massive due to the significant demand for energy sources for industrialization and electricity generation. Recently, the electricity consumption has been too excessive, and power rationing has to be implemented in China to reduce methane and carbon dioxide emissions. This is because most of the power supply is still derived from coal due to cheap production costs and availability. Therefore, alternative energy sources, especially those that are renewable and cleaner, can be used to replace coal as the primary energy provider to reduce carbon emissions. For instance, energy-rich agricultural biomass can be used as one of the alternative energy sources for industrial production or electricity generation.

Agricultural biomass is a consistent by-product derived from farming production. Most of these waste biomasses are not utilized efficiently; instead, these biomasses are usually left to decompose or burned in the open air, especially in developing countries (Tripathi et al. 2019). The continual increment in agricultural biomass production will pose a greater risk to human health and the environment. For instance, statistics showed that the open burning of biomass contributed to 18% of the total global emission of CO2, together with large quantities of particulates and soot (Jain et al. 2014). On the other hand, uncontrolled biomass disposal on land led to water pollution, eutrophication, and the production of biomass-induced microflora in the soil. This microflora will emit NO and N2O greenhouse gases with higher global warming potential than CO2 (Tripathi et al. 2019). One of the major agricultural biomasses is oil palm (Elaeis guineensis), which is an estate crop commodity and oil-producing plant that is produced and consumed globally. Palm oil has been in the prime position in the vegetable oil world market, with an annual production of 30 million tons since 2004, registering an annual growth rate of 8% (Hambali and Rivai 2017). Nonetheless, the entire supply chain to produce palm oil generates a few different biomasses compared to other crops such as soybean, rice, maize, and wheat. For example, oil palm fronds (OPFs) and oil palm trunks are produced during harvesting oil palm fruits and replanting activities. In the process of pressing fresh fruit bunches (FFBs) to produce palm oil, several biomasses are produced, such as palm mesocarp fiber (PMF), palm kernel shell (PKS), and oil palm empty fruit bunch (EFB) (Hambali and Rivai 2017). Palm oil mill effluent is also one of the wastes produced in the wastewater treatment process from palm oil mill. Other than that, palm oil sludge (POS) is one of the solid wastes from the wastewater treatment process that can be valorized after separation. In the current practice, most of these palm oil biomasses and wastes are usually disposed of at a landfill or incinerated to generate energy.

One method to efficiently utilize the energy embedded in the biomass is through thermochemical conversion. Thermochemical conversion is the decomposition of organic materials to produce liquid biofuel, solid fuels, and gaseous products (Halder and Azad 2019). There are a few thermochemical conversion technologies, such as direct combustion, pyrolysis, gasification, and hydrothermal liquefaction. The latter three have greater advantages in liberating the energy embedded in the biomass waste. Combustion is a highly exothermic chemical reaction that burns fuel in the presence of oxygen to generate heat or electrical energy (Kohse-Höinghaus 2021). On the other hand, gasification is the partial oxidation of solid fuels that react at a temperature range of 550–1000 °C, leading to mostly syngas or synthesis gas and trace of ashes (Klinghoffer and Castaldi 2013). Gasification differs from combustion, where it can be used to generate various forms of energy. The main products of gasification are combustible gases such as H2, CO, CH4, and CO2. These gases can be further converted into other gaseous or liquid fuels and chemicals such as ethanol and ethylene, which are the basic block for other chemical syntheses. Hydrothermal liquefaction is a relatively low-temperature process (300–400 °C) as compared to other thermochemical conversion but requires high pressure (40–200 bar) to convert wet biomass into solid biochar, liquid bio-oil, and combustible gases in the presence of hydrogen and catalyst (Milledge et al. 2014). This process also incurred a higher operating cost than other thermochemical conversions route due to its high-pressure operating condition (Gollakota et al. 2018; Milledge et al. 2014), and it is unfavorable for dry biomasses.

Pyrolysis, on the other hand, occurred without oxygen to produce solid biochar, liquid bio-oil, and combustible gases at a temperature range of 300–700 °C. Pyrolysis is further separated into two major types: fast and slow. Slow pyrolysis is carried out at a temperature of about 300 °C and requires a few hours to complete, leading to biochar as the main product (Djandja et al. 2020). In contrast, fast pyrolysis involves rapid heating of fuels at low residence time (< 1 s) at about 500 °C, leading to high composition of bio-oil products (Klinghoffer and Castaldi 2013). Both technologies produce different products, leading to different applications; however, slow pyrolysis usually reduces the process efficiency (Sipra et al 2018). In fact, among the various pyrolysis products, the bio-oil is the major product obtained from the fast pyrolysis process that attracted great interest as it can be used as liquid fuel in vehicles after several treatments and upgrading via hydrotreatment (Li et al 2020). Therefore, fast pyrolysis process will be the focus in this study. It is also regarded as the most promising approach as an estimation of 75 wt% bio-oil can be produced and applied in many applications directly or after upgradation (Inayat et al. 2022; Czernik and Bridgwater 2004). Several life cycle assessment (LCA) studies have proven that the biofuels produced from fast pyrolysis can reduce the greenhouse gas emission from vehicles by 51% to 96%, depending on the type of feedstock, pyrolysis technology, and pyrolysis yield (Han et al. 2013). In the terms of economic analysis, few studies have concluded that the production of bio-oil can be economically viable. For instance, Diehlmann et al. (2019) have demonstrated the economical viability of production of bio-oil from rice straw where 46–65% of the biomass is converted. Wang et al. (2019) have concluded that a manufacturing cost of $3 per kg of cotton stalk is achieved for a production capacity of 18,000 tons bio-oil per year. It is also worth noting that different sources of raw materials, different pretreatments and upgrading methods, and recycling techniques would have significant impacts on the economic feasibility of the production of bio-oil (Inayat et al 2022).

There are various types of pyrolysis technologies, including conventional pyrolysis, microwave pyrolysis, and thermo-catalytic reforming (TCR) pyrolysis. Conventional pyrolysis employs an external heating source to transfer heat to the material through a surface that depends significantly on the material physical properties, such as density, heat capacity, and thermal diffusivity. In contrast, microwave pyrolysis employs microwave heating that interacts with the biomass via electromagnetic wave (Beneroso et al 2017). This provides a direct electromagnetic energy transfer to the material, leading to volumetric and instantaneous heating regardless of the size of the material (Bermúdez et al 2015). Furthermore, the liquid product yield from microwave pyrolysis is higher compared to conventional pyrolysis, with the expense of greater capital cost incurred in power consumption (Sivagami et al 2021). On the other hand, TCR pyrolysis operates between fast pyrolysis and slow pyrolysis region, where the reaction temperature is similar to fast pyrolysis but with a lower heating rate and longer residence time (Schmitt et al 2019). TCR pyrolysis is also incorporated with a post-reforming process to improve the bio-oil quality. The catalyst required for the reforming process, biochar, is derived from the pyrolysis process (Neumann et al 2015). The combination of these processes in TCR pyrolysis converts the biomass waste into hydrogen-rich syngas, char without volatiles, and high-quality bio-oil with high heating value owing to its high carbon content, low water, and oxygen content (Schmitt et al 2019). Figure 1 demonstrates the block flow diagram for the three mentioned pyrolysis technologies to show the significant difference between unit operations and process pathway configurations.

Fig. 1
figure 1

Block flow diagram for a conventional pyrolysis, b microwave pyrolysis, and c thermo-catalytic reforming pyrolysis

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In Malaysia, 77% of the palm oil mills use combustion technology via a boiler or combined heat and power systems. In comparison, only 5% of the palm oil mills use advanced thermochemical conversion technologies such as gasification, pyrolysis, or hydrothermal liquefaction for self-consumption power generation (Umar et al 2013). Several drawbacks in switching to advanced thermochemical conversion technologies that bring better energy efficiency for the palm oil mills include the insufficient quantity of feedstocks, especially for smallholder palm oil mills. On the other hand, large players such as government agencies and multinational companies with large biomass feedstocks prefer to sell them at an attractive buying price or use them for other purposes such as mulching (Hamzah et al 2019). Furthermore, the shifting to advanced thermochemical conversion technologies requires a high investment cost. Therefore, it is not economically viable as the available boiler capacity can sustain the palm oil mills’ daily operation (Umar et al 2018).

In the current practice, most palm oil mills utilize only PMF and PKS as the boiler feedstock. However, other potential feedstocks, such as EFB, OPF, and POS, can also be converted to energy and fuel via thermochemical conversion (Hamzah et al 2019). Nevertheless, various biomass feedstocks available in the palm oil industry are comprised of different amounts of organic content and different chemical composition, thus leading to different energy contents. On the other hand, the moisture content of various palm oil biomasses is not the same. Hence, the energy required to achieve the optimal moisture content for thermochemical conversion is very much dependent on it. Furthermore, different pyrolysis technologies have diverse process configurations, resulting in different pyrolysis products’ yield. On the bright side, synergistic effect may occur when different palm biomass is considered for pyrolysis (Vasu et al 2020). All the factors mentioned above will significantly impact and should be considered during the designing stage of a new pyrolysis plant for energy production from biomass waste.

To date, no studies have considered multiple distinct pyrolysis processes or technologies in the past, but few have analyzed and compared between the type of pyrolysis (i.e., slow and fast pyrolysis) (Adelawon et al 2021; Zhao et al 2020). Multi-objective optimization is an effective tool utilized widely in designing an efficient yet sustainable system for various processes, especially the cases that involve conflicting objectives. In general, maximizing the profit and minimizing the capital and operating cost are essential for the system to be economically viable while minimizing the life cycle environmental emission of the process. The minimization of life cycle environmental emission is significant as greater awareness has been made on sustainability recently. This tool can be applied for valorizing palm biomass feedstocks by considering numerous pyrolysis technologies.

In the past, various publications have been done on pyrolysis products. For instance, Wu et al. (2019) proposed a mathematical model to optimize the co-processing of bio-oil and vacuum gas oil in the fluid catalytic cracker process by choosing the optimal biomass feedstock and bio-oil production process. In summary, the optimal biomass feedstock is pulpwood with fast pyrolysis adopted as the bio-oil production process in a minimized total annualized cost objective function. On the other hand, Gebreslassie et al. (2013) proposed bicriteria optimization to maximize net present value and minimize global warming potential for the hydrocarbon biorefinery via fast pyrolysis with hydrotreating, hydrocracking, and hydrogen production of hybrid poplar feedstock. Zhang et al. (2014) further expanded their work by introducing various hydrogen production technologies, hydro-processing options, and catalysts selection. Furthermore, Yuan and Eden (2016) proposed a superstructure optimization of integrated fast pyrolysis-gasification for liquid fuels and propylene production. Unlike Wu et al. (2019)’s research which focuses on the biomass feedstock and bio-oil production process, Yuan and Eden (2016)’s, Zhang et al. (2014)’s, and Gebreslassie et al. (2013)’s research focus on the downstream pyrolysis process that involves various hydrotreatment of bio-oil, hydrogen production technologies, and liquid fuel production. To add on, Zhao et al. (2020) investigated the economic and life cycle optimization of the poultry litter supply chain by considering various fast and slow pyrolysis processes into biofuel and biochar.

The objective of this study is to address the research gap on the comparison of multiple distinct pyrolysis process pathways with respective to the multiple palm biomass as pyrolysis feedstocks. Factors include multiple biomass feedstocks derived from the palm oil industry such as OPF, PMF, PKS, EFB, and POS and numerous pyrolysis technologies that include conventional pyrolysis, microwave pyrolysis and TCR pyrolysis will be accounted for via superstructure optimization to identify the optimum biomass selection and pyrolysis route with the best economic and environmental performance. The remainder of the work is organized as follows. The next section provides the problem statement of this study and the process overview of various pyrolysis technologies and palm oil biomasses derived from the palm oil mill industry. This is followed by the multi-objective mixed-integer linear programming (MILP) model formulation that addresses economic and environmental objectives. The results and discussion are presented with two different scenarios proposed and a concluding remark is provided.

Problem Statement

The optimization issue to be solved in this work is to utilize various biomass feedstocks under the palm oil mill industry, which include palm mesocarp fiber (PMF), palm kernel shell (PKS), oil palm empty fruit bunches (EFBs), oil palm frond (OPF), and palm oil sludge (POS), that will undergo various pyrolysis technologies such as conventional pyrolysis, microwave pyrolysis, and thermo-catalytic reforming (TCR) pyrolysis to produce bio-oil and biochar. The physical properties of these palm biomass are provided in Table 1. This work aims to determine the optimum configuration of oil palm biomass feedstocks and the pyrolysis technologies that will lead to maximizing the total annualized profit (TAP) and minimizing the global warming potential (GWP) simultaneously.

Table 1 Properties of palm biomass and bio-oil in this study
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The process overview of the pyrolysis of various palm oil biomass feedstocks derived from palm oil mills is shown in Fig. 2. First, the different types of palm biomass feedstocks are collected from the plantation site and palm oil mill, then transported to the pyrolysis site to convert the palm biomass into biochar and bio-oil as the main products. Three different pyrolysis processes are analyzed in this work: conventional pyrolysis, microwave pyrolysis, and TCR pyrolysis. Different pyrolysis technologies will eventually lead to different yield of biochar and bio-oil, which is also depending on the feedstocks used. Furthermore, the working principle of the pyrolysis processes differs from one another. Thus, different unit operations are installed, directly affecting the process’s capital cost and energy consumption. As demonstrated in Fig. 1, the conventional pyrolysis method includes a drying process that is not required in microwave pyrolysis because microwave heating uses radiation heat instead of convection heat. On the other hand, the TCR pyrolysis method differs from the conventional pyrolysis method, where the primary reaction is divided into two parts—intermediate pyrolysis and post reforming process. This novel method is said to improve the bio-oil quality in terms of lower oxygen content (Hornung et al. 2016). This led to a less intense hydrotreating process for the bio-oil upgrading to liquid fuels. A detailed process explanation for each of the unit operations is provided in Appendix B.

Fig. 2
figure 2

Process overview of pyrolysis pathways with respect to the different biomass generated in palm oil mill

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General Model Formulation

The problem for the optimal pyrolysis process considering multiple palm biomass feedstocks is solved via a multi-objective mixed-integer linear programming (MILP) model. Detailed equations and notation are provided in the “Supplementary Information.”

In the MILP model, two conflicting objective functions are introduced: maximizing the total annualized profit (TAP) and minimizing the global warming potential (GWP). These objective functions are subjected to several constraints, including the supply of five different types of feedstocks, pyrolysis facilities, economic, and environmental constraints. The outline of the model is shown as follows:

$$\begin{array}{lc}\mathrm m\mathrm a\mathrm x\;\;\;\mathrm T\mathrm o\mathrm t\mathrm a\mathrm l\;\mathrm a\mathrm n\mathrm n\mathrm u\mathrm a\mathrm l\mathrm i\mathrm z\mathrm e\mathrm d\;\mathrm p\mathrm r\mathrm o\mathrm f\mathrm i\mathrm t\\\mathrm m\mathrm i\mathrm n\;\;\;\;\mathrm G\mathrm l\mathrm o\mathrm b\mathrm a\mathrm l\;\mathrm w\mathrm a\mathrm r\mathrm m\mathrm i\mathrm n\mathrm g\;\mathrm p\mathrm o\mathrm t\mathrm e\mathrm n\mathrm t\mathrm i\mathrm a\mathrm l\\\begin{array}{lc}\mathrm s.\mathrm t.\;\;\;\;\mathrm E\mathrm c\mathrm o\mathrm n\mathrm o\mathrm m\mathrm i\mathrm c\;\mathrm c\mathrm o\mathrm n\mathrm s\mathrm t\mathrm r\mathrm a\mathrm i\mathrm n\mathrm t\mathrm s\\\;\;\;\;\;\;\;\;\;\;\mathrm E\mathrm n\mathrm v\mathrm i\mathrm r\mathrm o\mathrm n\mathrm m\mathrm e\mathrm n\mathrm t\mathrm a\mathrm l\;\mathrm c\mathrm o\mathrm n\mathrm s\mathrm t\mathrm r\mathrm a\mathrm i\mathrm n\mathrm t\mathrm s\\\begin{array}{lc}\;\;\;\;\;\;\;\;\;\mathrm P\mathrm y\mathrm r\mathrm o\mathrm l\mathrm y\mathrm s\mathrm i\mathrm s\;\mathrm f\mathrm a\mathrm c\mathrm i\mathrm l\mathrm i\mathrm t\mathrm i\mathrm e\mathrm s\;\mathrm c\mathrm o\mathrm n\mathrm s\mathrm t\mathrm r\mathrm a\mathrm i\mathrm n\mathrm t\mathrm s\\\;\;\;\;\;\;\;\;\;\mathrm P\mathrm a\mathrm l\mathrm m\;\mathrm b\mathrm i\mathrm o\mathrm m\mathrm a\mathrm s\mathrm s\;\mathrm f\mathrm e\mathrm e\mathrm d\mathrm s\mathrm t\mathrm o\mathrm c\mathrm k\;\mathrm c\mathrm o\mathrm n\mathrm s\mathrm t\mathrm r\mathrm a\mathrm i\mathrm n\mathrm t\mathrm s\end{array}\end{array}\end{array}$$

To deal with the multiple objectives function feature of the model, an ɛ-constraint method is introduced. This method is frequently used to handle problems related to multi-objective optimization by presenting Pareto-optimal solutions due to its simplicity (Zhao et al 2020), and it can obtain all the Pareto-optimal solutions, including those in the convex space of the objective space (Laumanns et al 2006). To obtain the Pareto-optimal curve in multi-objective function problems, one of the objective functions, the GWP emission, in this case, is converted into an ɛ-constraint. Do note that either one of the two objectives can be selected for the conversion to ɛ-constraint. Additional information regarding the ɛ-constraint is provided in the “Supplementary Information.” After converting the environmental objective function to a corresponding ɛ-constraint, the multi-objective MILP model is reformulated into a single-objective MILP model, which is shown as follows:

$$\begin{array}{lc}\mathrm{max\;\; Total\; annualized\; profit}\\ \mathrm{s}.\mathrm{t}.\;\;\;\upvarepsilon -\mathrm{Constraint\; on\; global\; warming\; potential }\\ \;\;\;\;\;\;\;\;\;\begin{array}{lc}\mathrm{ Economic\; constraints }\\ \mathrm{ Environmental\; constraints}\\ \begin{array}{l}\mathrm{ Pyrolysis\; facilities\; constraints}\\ \mathrm{ Palm biomass\; feedstock\; constraints}\end{array}\end{array}\end{array}$$

In this study, two scenarios are demonstrated. The first scenario focused on maximizing the TAP and minimizing the GWP emission without considering the constraint on the palm biomass feedstock distribution (i.e., there is no minimum feed required for each of the biomass). Therefore, the model would favor single biomass feedstock in achieving the most optimum result. In the second scenario, the state of Johor in Malaysia is selected as the case study by considering the production of palm biomass based on the plantation hectare in the selected location. The second scenario considers the actual production of palm biomass in the state of Johor in Malaysia. Thus, utilizing multiple types of biomasses available is the limiting constraint in the MILP model. The oil palm plantation area is about 0.8 million hectares in the state of Johor, and 15 million tons of fresh fruit bunches are processed annually (Aljuboori 2013). The pyrolysis plant aims to utilize 20% of the biomass available in the selected location. Table 2 shows the production rate of various palm biomass annually based on the plantation size.

Table 2 Production rate of various palm biomass in the state of Johor, Malaysia (Aljuboori 2013; Yusoff 2006)
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Results and Discussion

Scenario I—Multi-objective Optimization of Single Biomass Feedstock for the Pyrolysis Process

The model is solved using LINGO 13.0 software with a lapse time of one second running on a Windows 10 Home Edition 64-bit operating system personal computer with Intel Core i5-4590 at 3.30 GHz and RAM of 8.00 GB.

For the first scenario, the Pareto-optimal solution for the multi-objective optimization of economic and environmental criteria of pyrolysis process from palm mill feedstock is illustrated in Fig. 3. The Pareto-curve represents the trade-off between the economic and environmental objectives. In general, greater annualized profit and lesser environmental emissions are preferred. Thus, the region above the curve is infeasible region, while the region below the curve is sub-optimal.

Fig. 3
figure 3

Pareto-optimal curve representing the trade-off between economic and environmental objectives for Scenario I

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Six Pareto-optimal solutions (Point A, Point B, Point C, Point D, Point E, and Point F in Fig. 3) are selected for further analysis. Point A constitutes the lowest environmental emission. However, the profit generated from the pyrolysis plant is also the lowest. For Point B and Point C, as compared to Point A, a slight trade-off in the environmental emission can yield a steep increase in the profit, mainly by switching the palm biomass feedstock from EFB to PKS and OPF. EFB exhibits properties such as higher moisture content and lower bio-oil yield as compared to PKS and OPF, which require higher energy to remove the moisture of the biomass, resulting in a lesser profit. However, in terms of environmental criteria, EFB is superior to PKS and OPF in biomass size. Thus, lesser energy is required to further reduce the size of EFB to the necessary condition of pyrolysis reaction. The energy consumed in grinding for size reduction is greater than the energy of drying. Comparing Point B and Point C, the reason for the superiority of OPF towards PKS in terms of the economic criteria is the greater bio-oil yield which led to better profit. However, in terms of environmental criteria, the PKS is far better than OPF due to lower moisture content and lower water content in bio-oil. This led to less intensive pretreatment and post-treatment to remove the water content in the biomass and bio-oil, respectively, thereby saving energy and reducing carbon emission. Point A, Point B, and Point C employ the microwave pyrolysis route that is more environmentally friendly, which will be further discussed in a later section involving the environmental emission breakdown of pyrolysis unit operations.

Point D, Point E, and Point F are analogous to Point A, Point B, and Point C. The difference is that the former points exhibit both higher economic returns and greater environmental impact due to a different pyrolysis unit being used. Point A, Point B, and Point C employed microwave pyrolysis, while Point D, Point E, and Point F employed conventional pyrolysis. Conventional pyrolysis has a lower start-up cost than microwave pyrolysis, which leads to a greater profit margin. Point F represents the solution with the greatest profit but at the cost of the highest environmental emission. It is good to note that all the solutions that lie on the curve, including Point A, Point B, Point C, Point D, Point E, and Point F, are optimal solutions, where the selection of the point is based on the preference between the two objectives. Solutions in the left region (i.e., Point A, Point B, and Point C) would place importance on environmental emission as a top priority. In contrast, solutions in the right region (i.e., Point D, Point E, and Point F) aims to maximize the profit of the pyrolysis plant.

Microwave pyrolysis does not require tedious pretreatment processes such as grinding and drying, which consume some of the process energy, the application of microwave process in scale-up operations is still lacking in technical information and detailed design. Thus, a higher capital cost is estimated for microwave pyrolysis, leading to a lower profit margin. It is also interesting to note that PMF and POS are not selected as the feedstock in the model mainly due to PMF fetching a high water content in bio-oil. This renders the overall lesser bio-oil yield, resulting in loss generated for the plant. Similarly, POS favors biochar yield to bio-oil yield, where the profit generated for biochar is far lesser than treated bio-oil. This resulted in meeting the same fate with PMF biomass in incurring losses if it is used as the biomass feedstock for pyrolysis.

The economic breakdown for the various pyrolysis processes is demonstrated in Fig. 4 with information such as TAP, total annualized revenue, total annualized capital cost, and total annualized operating cost. Note that the biomass feedstock is constant while varying the pyrolysis technologies. Similar to the Pareto-optimal curve shown in Fig. 3, conventional pyrolysis has a higher TAP, which places the process that selected conventional pyrolysis on the right region of the Pareto-optimal graph. This is followed by microwave pyrolysis and then TCR pyrolysis. Since TCR pyrolysis has the lowest profit generated, as shown in Fig. 4, this technology is not selected, hence does not appear in the Pareto-optimal graph. One of the reasons that led to the lowest TAP for TCR pyrolysis is the reforming process resulting in higher capital costs and operating costs than conventional pyrolysis.

Fig. 4
figure 4

Economic breakdown of various pyrolysis processes for Scenario I

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Since some of the biochar is used as the catalyst in the catalytic reforming unit operation, the overall yield of biochar is reduced, leading to lower total annualized revenue (Schmitt et al 2019). On the other hand, microwave pyrolysis has the highest total annualized revenue from the high yield of pyrolysis products due to its unique thermal gradients. The exceptional cooler surroundings during microwave heating allows the carbohydrate derivatives to be preserved in larger quantities than in other pyrolysis processes (Beneroso et al 2017). However, the TAP of microwave pyrolysis is offset by the higher total annualized capital cost and operating cost, mainly due to a higher estimation of the unit operations of microwave pyrolysis. A few of the factors supporting this statement include the lack of large-scale operation data, even from demonstration plants, in which the technical risks have not been evaluated thoroughly and mitigated (Buttress et al 2016). This resulted in the capital cost being a vital sensitivity variable due to unforeseen uncertainties (Wang et al 2015). Haeldermans et al. (2020) have concluded that conventional pyrolysis is more viable than microwave pyrolysis as it is a simpler and more established technology. Furthermore, Kim et al. (1999) have reported that the capital cost of microwave hardware is $1000–$2000 per kW of installed power, which is higher than the other conventional heating hardware. Nevertheless, the energy efficiency of the microwave heater is much better than conventional pyrolysis that uses a burner, where the efficiency improvement can be up to fourfold (Binti Mohd 2017). This directly reduces the energy consumption per unit mass of biomass during the pyrolysis process.

Table 3 shows the breakdown of environmental emission for each unit operation with respect to the type of pyrolysis process. In terms of environmental emission, the microwave pyrolysis pathway yields the most negligible environmental impact, followed by conventional pyrolysis and then TCR pyrolysis, which contribute almost the same emission at about 650 kg CO2 equivalent per unit mass of biomass. By breaking down the pyrolysis pathways into a single unit, the grinder unit operation contributed most to the environmental emission, which is one of the essential pretreatment sections that must be done so that the heating efficiency and uniformity of biomass during the pyrolysis process are improved. Furthermore, grinding action consumes electrical energy, which contributes more CO2 emission per kW of energy than heat energy powered by natural gas in the life cycle assessment perspective. Since microwave pyrolysis employs radiation heating, the size of biomass does not have a great impact on the heating process. Thus, the emission is lower as it does not require an energy-intensive grinding operation. The second unit operation that has the most significant impact on global warming is the condenser, which also consumes tremendous electrical energy during the separation process of bio-oil and biogas. These two unit operations should be the top priority during process optimization or intensification to reduce pyrolysis emissions. Looking solely at the pyrolysis process in Table 3, microwave pyrolysis contributed more emission than conventional pyrolysis, which has the least emission generated, followed by TCR pyrolysis when both pyrolysis and reforming process are combined. Experiment studies have shown that microwave pyrolysis consumes 75% lesser energy than conventional pyrolysis (Binti Mohd 2017). This is because microwave heating is an internal heating mechanism that leads to rapid and selective heating, as compared to other pyrolysis processes, which require a longer residence time to achieve the targeted temperature due to conductive and convective techniques (Gautam et al 2019; Liew et al 2019). Additionally, the conversion efficiency of electrical energy to heat energy for microwave heating is above 80%, thus minimizing the energy loss (Osepchuk 2002). However, because microwave pyrolysis operation requires electricity, the emission from microwave pyrolysis is seen greater than conventional pyrolysis.

Table 3 Environmental emission breakdown of various pyrolysis processes for Scenario I
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Scenario II—Feedstock Blending with a Constraint on Biomass Availability Based on Location

A capacity of 2000 tons per day of pyrolysis facility is introduced to fully cover the 20% of the biomass available in the state of Johor. The biomass generated in the area will be transported to the pyrolysis facility in the state itself. The environmental factor is analyzed in this scenario, but not the economic factor. This is because the selection of biomass does not affect the capital cost but only the operating cost, which has a linked relationship with the environmental emission. The environmental emission shown in Fig. 5 exhibits a trend as in Table 3 where microwave pyrolysis technology has the lowest emission, followed by conventional pyrolysis and then TCR pyrolysis. It is interesting to note that the model will generate different optimal blending of biomass when there is a constraint on feedstock availability in a particular area. From Table 3, the available palm biomass based on 0.8 million hectares in the state of Johor is 715 tons, 482 tons, 679 tons, 4296 tons, and 294 tons of PMF, PKS, EFB, OPF, and POS, respectively. Given a pyrolysis plant with a 2000-ton feed capacity, feedstock blending using various biomasses must be considered to fulfill the required capacity. PKS, EFB, and POS are fully utilized for conventional and microwave pyrolysis, with the remaining sourced from OPF. Feedstock blending is proved to be advantageous because of the variety in biomass options, lower threat, and lower carriage costs (Oasmaa et al 2010). The selection of biomass also followed the priority of POS and then EFB, PKS, and OPF, which is similar to the Pareto-optimal chart in Fig. 3 based on environmental emission. The POS has the least environmental emission due to the low bio-oil yield; hence, lesser treatment is needed. However, as mentioned above, the high acquisition cost and the low yield of bio-oil from POS biomass would cause the plant to incur losses if it is used as the sole feedstock for pyrolysis. The case might be different when there is a mixture of biomass. The overall environmental emission can be reduced, and other biomass that generates profits can cover the losses. The PMF biomass is not selected as the feedstock because of the high water content in bio-oil that requires greater energy for post-treatment, leading to higher cost. Another reason for this is the availability of OPF in large quantities that can fulfill the capacity of the pyrolysis plant. However, the drawback of using OPF is the accessibility of this biomass as it needs to be collected on the plantation site compared to other biomasses that can be obtained immediately after the mill operation.

Fig. 5
figure 5

Biomass feed and environmental emission for various pyrolysis processes in Scenario II

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For TCR pyrolysis, OPF is not selected as the fourth biomass; instead, PMF is selected to be used. This is because TCR pyrolysis can yield bio-oil properties with lesser water content in bio-oil with an additional reforming section (Hornung et al 2016). Therefore, less intensive hydrotreatment is required at the post-treatment stage to remove the water content in the bio-oil. The study performed in Scenario II has shown that different biomasses would impact the environmental emissions. Furthermore, based on the selected pyrolysis technology, certain biomasses would perform better in reducing emissions and cost savings. Figure 6a, b, and c demonstrates a clearer picture of the input and output of mass and energy balance for conventional pyrolysis, microwave pyrolysis, and TCR pyrolysis for the case study in Scenario II.

Fig. 6
figure 6

Mass and energy balance process flow diagram for Scenario II: a conventional pyrolysis, b microwave pyrolysis, and c thermo-catalytic reforming pyrolysis

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A sensitivity analysis is performed by varying the biomass feed for Scenario II to mimic the context of a disrupted biomass supply, as shown in Fig. 7. For example, the biomass can be used to produce other potential products with better economic yield. Therefore, other palm biomass feeds must be utilized to fulfill the capacity of the pyrolysis plant. Is it reasonable to note that the maximum supply of the type of biomass also depends on the availability in the area. In Fig. 7, the first case study, denoted as 1, is similar to the conventional pyrolysis in Fig. 5, registered the lowest emission when all the biomass feed is available, and PKS, EFB, and POS are fully utilized. For case study 2, PKS is not supplied to the pyrolysis facility, and the remaining capacity is supported by OPF, leading to a higher emission than in case study 1. In case study 3, EFB is not supplied to the pyrolysis facility. Due to the massive ample supply available for OPF, it can cover the remaining deficit required for the pyrolysis facility. However, the GWP in case study 3 is also the highest, leading to the conclusion that EFB is one of the cleanest biomass feeds for the pyrolysis process as it has the lowest emission due to the size of EFB is smaller as compared to other biomass. Case study 4 does not include OPF in the pyrolysis facility; thus, PMF has to be utilized to cover the remaining capacity. The usage of PMF has a slightly higher GWP as compared to case study 1. The final case study 5 does not include POS for the pyrolysis plant. This sensitivity analysis has proven that different biomass feeds would have an adverse effect on the GWP.

Fig. 7
figure 7

Sensitivity analysis of various biomass feed for conventional pyrolysis

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Conclusion

In this work, a bioenergy complex consisting of multiple palm biomass feedstocks available in the palm oil supply chain such as palm mesocarp fiber (PMF), palm kernel shell (PKS), oil palm empty fruit bunches (EFBs), oil palm frond (OPF), and palm oil sludge (POS) which fed into various pyrolysis technologies such as conventional pyrolysis, microwave pyrolysis, and thermo-catalytic reforming (TCR) pyrolysis is proposed. A multi-objective mixed-integer linear programming (MILP) model was formulated to determine the optimal designs of the palm biomass supply chain by considering both the economic and environmental objectives. This multi-objective optimization problem is solved via an ɛ-constraint method. The proposed model is robust for planning bioenergy complexes, especially those involving multiple biomass feedstocks. Furthermore, this model is generic as it can be applied not only to different regions with different biomass availability but also to other agricultural industries.

In the multi-objective optimization of single biomass feedstock for the pyrolysis process, Scenario I, some insights are obtained from the results. First, a Pareto-optimal curve has shown a clear trade-off between two conflicting economic and environmental objectives: the total annualized profit and global warming potential, respectively. The most profitable solution achieves an annualized profit of $237 per ton of OPF converted with an emission of 628 kg CO2 equivalent per ton of OPF consumed. On the other hand, the most environmentally sustainable solution generates an annualized profit of $122 per ton of EFB converted with an emission of 132 kg CO2 equivalent per ton of EFB consumed. Scenario II, which reflects a case study on the palm oil industry in Johor of Malaysia, has also demonstrated the selection of biomass during feedstock blending when a constraint on biomass feedstock availability is pre-defined. The selection of palm biomass feedstock based on the lowest GWP in ascending order is POS, followed by EFB, PKS, OPF, and then PMF. However, the type of pyrolysis technologies directly impacts the feedstock blending where certain feedstocks can create synergy effects with the pyrolysis units that result in either lower emission or better cost savings.

For the recommendation of future research, the price fluctuation of the feedstock can be considered as the price will directly impact the plant’s profitability. Furthermore, the debottlenecking of the unit operations with the greatest emission, for instance, the grinding and condenser unit operation, by process optimization and process intensification, can aid in reducing the total carbon footprint on the environment.