焙燒和裂解是可再生生質物燃料（生物燃料）生產中典型的熱化學轉化（TC）技術。焙燒也被稱為溫和裂解，是在 200-300 °C 之間的溫度下發生的過程，而裂解過程則是發生在溫度高於 350 °C 時。在兩者之間，有一個焙燒和裂解的過渡區域，發生在 300-350 °C。焙燒和裂解過程通常在無氧環境下進行。氮氣 (N2) 等非氧氣體為典型經常用於焙燒和裂解過程的惰性氣體。
生質物燃料為液態、固態和氣態來至生質物的燃料，而生質物被定義為來自植物和動物的可再生資源。生質物有多種類型，包括主要成分為纖維素、半纖維素和木質素的木質纖維素生質物、含有蛋白質、脂類、碳水化合物、一些色素和多種維生素的藻類生質物、包括糞便、皮膚、脂肪和貝殼的動物性廢棄物，以及含有多種生質物混合的生質物廢物，例如農業廢棄物、森林殘餘物、工業廢棄物、住宅廢棄物等。生質物被認為是用於產生生質能源且同時減少對化石燃料等不可再生能源的過度依賴的可再生材料。生質物原料 (BMF) 使用的其中最大優勢包括它為非常豐富和碳中性材料。
本研究使用了兩種類型的生質物原料，包括微藻和生質物廢棄物。微藻是一種小尺寸 (µm) 的藻類生質物。微藻為具有潛力的 BMF，因為它不需要土地生長，有快速的生長速度，並且有高生產力。廢蘑菇堆肥 (SMC) 是一種木質纖維素農業廢棄物，在收穫季節後從蘑菇農場產生。SMC在台灣的產量非常豐富。木質纖維素和藻類生質物通常含硫量低。使用生質物燃料（生物炭、生物油和生物合成氣）可以減少硫相關污染，例如 SOx。因此，將微藻和 SMC 的農業廢棄物轉化為生物燃料被認為是減少垃圾填埋量和實現循環生物經濟的合適策略。
焙燒和裂解被認為是優良的生質物轉化過程，因為它們需要較少的能量需求。焙燒主要生產為生物炭（一種富含碳的固體材料）、一些生物油和少量生物合成氣。裂解的主要產物則為生物油、部分生物炭和生物合成氣。此外，此研究利用信噪比和方差分析的統計方法來優化焙燒和裂解過程。
人工智慧 (AI)可被形容為像人一樣思考和行動的軟體計算程序。AI被設計來幫助社會解決複雜的任務，以取代大量的勞動力。人工智能廣泛應用於教育、企業、醫療、社交媒體和汽車等領域。在生質能源生產過程中，人工智能，尤其是人工神經網絡（ANN）經常被用於預測生質能生產、輔助優化等。
從文獻中發現，已有大量的研究針對微藻和農業廢棄物，但是，目前沒有研究關注微藻和農業廢棄物 SMC用於生物燃料生產。這項研究研究了微藻和農業廢棄物 SMC 的 TC過程。討論分為三個部分，包括提取微藻的裂解成分、SMC 的焙燒和裂解，以及 SMC 的單獨焙燒。
研究的第一部分透過熱重分析 (TGA) 來討論裂解過程中微藻的提取成分，並將其與動力學模型獨立平行反應 (IPR) 粒子群優化 (PSO)來研究微藻的化學反應速率和活化能。也測量了相互作用效應，即協同效應和拮抗效應（SE 和 AE）。正如前面所強調的，蛋白質的熱值（高熱值 - HHV）約為 5.33 MJ·kg−1，不適合用於生物能源生產。相較之下，脂質的HHV 約為 34.22 MJ·kg-1，而來自碳水化合物（葡萄糖、澱粉和混合物）的成分的 HHV 約為 15.37–15.84 MJ·kg-1，可作為生物能源的潛在材料。從轉化過程可發現物質的熱降解遵循碳水化合物、蛋白質、脂質的順序。動力學研究的三個偽組分模型中獲得超過 96% 的高擬合值，證明動力學模組是合適的預測模型。從相互作用分析的結果可發現，SE 估計佔模型碳水化合物的接近 50% (200–320 °C)。同時，透過觀察三種物質混合物的 TGA 曲線（理論和實驗），發現其可分為四個區域（I-強 SE、II-弱 AE、III-弱 SE 和 IV-強 AE）。
在研究的第二部分，SMC 的生物燃料轉化以催化-磁焙燒-裂解過程進行評估（催化劑：氧化鎂 – MgO；磁性劑：硫酸鐵銨 - FAS，NH4Fe(SO4)2 ；裂解方法：微波輔助加熱（or microwave-assisted heating – MAH)。田口方法被使用於優化和最大化目標函數；同時，ANN模型被使用於執行預測。田口方法的正交陣列則被用於實驗的多重控制。最大化的總生物燃料產量——生物炭產量和生物油產量的 TBY ——為 99.42%，而此結果是使用 355 µm 粒徑、30% 的催化劑、900 W 的微波功率和 30% 的磁性劑進行實驗所取得的。方差分析 (ANOVA) 的催化劑影響分析結果與平均信噪比 (S/N) 的評估結果一致。具有一個隱藏層的 ANN 對於生物炭和生物油預測的最大化結果 TBY 0.9999 和生物油 0.9998 分別表現出出色的準確性。整體而言，這些結果表示，使用 Quickprop的 ANN 可被用於生物能源領域作為預測模型的方法。
在研究的第三部分，SMC 的生物燃料轉化以 MAH 在焙燒條件下進行。本部分引進了“特定化學生質物能（bioexergy）-SCB”的新術語。 SCB 描述了生物燃料中包含的實際能量，為生物燃料表徵的重要參數之一。生質物洗滌被發現可降低灰分含量58.45%（酸洗）和 36.29%（水洗）。在轉化過程之前對生質物進行酸洗並在 540 W 微波功率下添加 35% Fe2O3 進行催化反應的實驗組合得出的最大化 SCB值約為 47.90 MJ·kg-1。 最佳條件下的SCB ，比較原料及處理後材料有著273.05%的提升，大約三倍。 ANN 模型的三個神經元和一個隱藏層方案的 3N-1HL 顯示出非常高的預測精度，即 R2=1。從該結果可判定指定模型對於預測 SCB 生物燃料是準確的。

Torrefaction and pyrolysis are the typical thermochemical conversion (TC) technology in renewable biomass fuel (biofuel) production. Torrefaction, also well-known as mild pyrolysis, is a process that occurs at a temperature between 200-300 °C. Meanwhile, the pyrolysis process occurs further than >350 °C. In between, there is a transitional area of torrefaction and pyrolysis process, which occurs at 300-350 °C. Torrefaction and pyrolysis processes are commonly performed in the absence of an oxidative environment. A non-oxidative gas such as nitrogen (N2) is a typical inert gas frequently utilized to accommodate the torrefaction and pyrolysis processes.
Biomass fuel (biofuel) is any type of fuel in liquid, solid, and gaseous phases derived from biomass-based materials. Biomass is defined as a renewable resource from plants and animals. There are several types of biomass including lignocellulosic-based biomass which mainly contains cellulose, hemicelluloses, and lignin; algal-based biomass which contains protein, lipids, carbohydrates, some pigment, and multi-vitamins; animal-based mostly waste includes manure, skin, fat, and shell; and biomass wastes which possess mix characteristic such as agriculture waste, forest residue, industrial waste, residential waste, etc. Biomass is considered a promising renewable material for generating bioenergy to reduce overreliance upon non-renewable energy from fossil fuels. Some of the advantages of biomass feedstock (BMF) usage including it is highly abundant on Earth and carbon neutral materials.
Two types of materials used in this study include microalgae and biomass waste. Microalgae is one type of algal-based biomass in a small size (µm). Microalgae are promising BMF as it no requires land to grow, possess a faster growth rate, and has high productivity. Spent mushroom compost (SMC) is one type of lignocellulosic agrarian waste that is produced from the mushroom farm after the harvesting season. It is recognized that SMC is highly abundant in Taiwan. Lignocellulosic and algal-based biomasses generally have a low sulfur content. Using biofuels (biochar, bio-oil, and bio-syngas) can reduce sulfur-related pollution such as SOx. In this regard, biofuel conversion from microalgae and agrarian leftovers of SMC is considered a suitable strategy to cut down the disposal of waste in landfill and achieve a sustainable circular bioeconomy.
Torrefaction and pyrolysis are considered suitable biomass conversion processes since they require less energy. Torrefaction produces mainly biochar (a carbon-rich solid material), some bio-oil, and slightly bio-syngas. Meanwhile, pyrolysis produces mainly bio-oil, some biochar, and bio-syngas. Moreover, the statistical analysis of the S/N ratio and ANOVA are utilized to optimize the torrefaction and pyrolysis processes.
Artificial intelligence (AI) is simply termed as a soft-computing program that thinks and acts like human beings. AI is programmed to assist society to solve complex tasks without requiring a huge workforce. AI is widely applied in education, corporate, healthcare, social media, and automobile. In bioenergy production, AI, especially artificial neural networks (ANN), is frequently utilized to predict bioenergy production, assist the optimization, etc.
The literature review suggests that numerous studies have been conducted on microalgae and agriculture waste. However, no study focuses on the extracted substances of microalgae and agriculture waste SMC for biofuel production. This study investigates the TC of biomass fuels from extracted microalgae and agriculture waste SMC. The discussions are segmented into three parts: pyrolysis of extracted microalgae components, torrefaction and pyrolysis of SMC, and solely torrefaction of SMC.
In the first part of the study, the extracted constituents of microalgae are investigated using the pyrolysis process via thermogravimetric analysis (TGA) and it is assimilated with the kinetics model, namely, the independent parallel reaction (IPR) particle swarm optimization (PSO) scheme to investigate the chemical reaction rate and activation energy of extracted microalgae. The interaction effects, namely, synergistic and antagonistic effects (SE and AE) are also measured. As highlighted, the calorific values (higher heating value – HHV) of all extracted substances demonstrate that protein with HHV of about 5.33 MJ·kg−1 is not suitable for directly used to generate bioenergy. Contrarily, a higher HHV of lipids accounted for about 34.22 MJ·kg−1, and some moderate HHV from carbohydrates (glucose, starch, and mixture) accounted for about 15.37–15.84 MJ·kg−1 are suggested prospective materials for bioenergy generation. The conversion process evokes that the thermal degradation of substances obeys the order of carbohydrates followed by protein, then finally, lipid. The high fitting value of over 96% is obtained from the three pseudo-components models of a kinetics study, demonstrating a modeled kinetics scheme is the appropriate predicting model. The interaction analysis unveils that the SE is estimated for almost 50% (200–320 °C) of the model carbohydrates. Concurrently, the TGA curvature (theory and experiment) of the blend of the three substances demonstrates that there are about four domains observed (I–strong SE, II–weak AE, III–weak SE, and IV–strong AE).
In the second part of the study, the biofuel conversion of SMC is evaluated using a catalytic-magnetic torrefaction-pyrolysis process (catalyst: magnesium oxide – MgO, magnetic agent: ferric ammonium sulfate – FAS, NH4Fe(SO4)2 via microwave irradiation (or microwave-assisted heating – MAH). The process is optimized and maximized using the Taguchi method. Meanwhile, the prediction is executed by the ANN model. The Taguchi orthogonal array is applied to arrange the multiple control of the trial. The maximum total biofuel yield – TBY of biochar yield and bio-oil yield is described for 99.42%, achieved by performing a sequencing experiment using 355 µm particle size, 30% of catalysts, 900 W of microwave power, and 30% of magnetic agents. The outcomes of the influence analysis of the catalyst from analysis of variance (ANOVA) agree with the mean signal-to-noise (S/N) ratio evaluation. ANN with one hidden layer exhibits excellent accuracy for the maximum TBY 0.9999 and bio-oil 0.9998 for biochar and bio-oil predictions, respectively. Overall, this result suggests that ANN using a scheme of applying a Quickprop is a suitable method for forecasting models in the bioenergy field.
In the third part of the study, the biofuel conversion of SMC is solely performed in the torrefaction process via MAH. A new term for “specific chemical biomass exergy (bioexergy) – SCB” is demonstrated in this part. SCB describes the actual energy consisted within biofuel. For biofuel properties, SCB is one of the essential parameters of biofuel characterization. Biomass washings may decrease the ash content by 58.45% (acid washing) and 36.29% (water washing). The combination of performing an acid wash on biomass prior to the conversion process combined with adding a 35% Fe2O3 for a catalytic reaction in 540 W of microwave power exhibits the maximum value of total SCB for about 47.90 MJ·kg-1. The improvement of the SCB from the optimum condition is relatively 273.05%, about three-fold from raw to treated materials. The 3N-1HL for three neurons and one hidden layer scheme of the ANN model shows remarkably high accuracy prediction that is accounted for R2=1. This result indicates that the assigned model is accurate for forecasting the SCB biofuel.

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