Fractional Factorial Analysis Research Articles

Simulating energy performance of buildings: a study using eQUEST and Energy Star® portfolio manager

ABSTRACT Commercial buildings consume significant energy in the United States and exhibit high potential for energy use reduction through retrofits. Benchmarking and energy simulation are well established tools in the industry to identify potential improvements and measure performance. Analysis to identify most sensitive retrofit parameters to energy performance can optimize investment and available energy savings. Presented study demonstrates methodology using a static model to determine sensitivity of building design and retrofit parameters with respect to energy performance. Calibrated simulation energy models (eQUEST) of two distribution centers (A, B) are presented. A fractional factorial analysis is conducted on retrofit parameters of efficiency measures targeting the highest energy consumers, and the results are benchmarked using Energy Star® Portfolio Manager. A custom Microsoft Excel® based simulation model is created to simulate occupancy levels, lighting, plug loads, and other equipment used in various spaces throughout the day. For Building A, efficient lighting was the most influential parameter for energy savings, carbon savings and benchmarking score; whereas, for Building B, HVAC efficiency was most influential for energy and demand controlled ventilation and economizers was most influential for benchmarking score. While retrofit projects can save energy and carbon emissions, variation in source-site ratios and state grid emissions, benchmarking scores may not always reflect equivalent improvement. State grid emissions factors, natural gas composition are difficult to model and hence not considered in this study. The synergistic analysis presented, emphasizes the importance of benchmarking and efficiency retrofits in promoting sustainable building practices to reduce energy consumption.

Analysis of factors influencing on performance of solid tires: Combined approach of Design of Experiments and thermo-mechanical numerical simulation

Excessive loading conditions and internal heat build-up are the major causes of failures in solid resilient tires. Both operational and design related factors can affect tire performance including internal stress generation and heat build-up. In spite of their limitations, experimental techniques are still the most common method of identifying the factors that affect tire performance. This study explores a combined approach of numerical modelling and Design of Experiments (DoE) to identify the impact of several key design and operational factors on the performance of solid tires under different temperature levels. First, 3D Finite Element (FE) static and thermal models of the solid resilient tires are developed by incorporating suitable temperature dependent hyperelastic material models and relaxation properties. The temperature-dependent mechanical behaviours of rubber material are obtained by using Dynamic Mechanical Analyzer (DMA) and the William Landel Ferry (WLF) parameters. The developed FE thermal model is used to predict the tire performance, including the tire blasting region, which is then validated with experimental data. Secondly, a two-level fractional factorial DoE analysis is developed using response readings of maximum stresses, deformations and contact pressure obtained from the validated FE thermal model. The factors used in the DoE include the number of rubber layers, applied load, rolling speed, ramp angle and aperture level. The results provide optimal operational and design parameters of the solid resilient tire at different environmental temperatures and show the potential for considerable stress reduction with minimal changes to tire deformation and contact area. These results highlight the capability of the combined FEM and statistical design approach to drastically reduce the number of physical experiments required for obtaining optimal tire performance thus saving significant time and cost.

The Effect of the Surface Roughness Characteristics of the Contact Interface on the Thermal Contact Resistance of the PP-IGBT Module

In this article, the correlation between the thermal contact resistance and the surface roughness characteristics of the contact interface in the press-pack insulated-gate bipolar transistor (PP-IGBT) modules during power cycling was studied by experimental measurements and finite-element (FE) simulation-based factorial design analysis. Thermal transient test technology was applied to examine the change in the thermal characteristic parameters of the PP-IGBT module. This shows that the increase in the thermal contact resistance of the Al metallization/emitter Mo contact interface occurs more dramatically during power cycling. A 3-D surface profilometer was used to evaluate the surface morphology parameters of the Al metallization/emitter Mo contact interface. The equivalent root-mean-square (RMS) roughness increases during power cycling, and the equivalent asperity slope and the equivalent spacing between asperities increase slightly. Additionally, the surface roughening in the corner area of the chip is more obvious than in other regions. A fractional factorial design analysis based on FE simulations was performed. The results indicate that the thermal contact resistance strongly depends on the main effects of the real contact area and the spacing between the asperities, and the RMS roughness and the asperity slope interaction.

Human developmental toxicity mechanism of polybrominated biphenyl exposure and health risk regulation strategy for special populations

Polybrominated biphenyls (PBBs) can bioaccumulate in nature and are toxic to humans. Long-time exposure to PBBs in pregnant women can lead to the birth of an infant with abnormal conditions. Hence, in this study, we used molecular docking, molecular dynamics, Taguchi experimental design, and fractional factorial experimental design to identify the developmental toxicity characteristics of 10 typical developmental toxic pollutants such as PBBs to which humans are frequently exposed. Furthermore, the correlation and sensitivity analyses of molecular developmental toxicity and structural parameters were performed. The molecular key structural parameters of the pollutants affecting human development were screened. Moreover, the supplementary food factors that could alleviate the developmental toxicity of pollutants were screened to develop supplementary food schemes to prevent or alleviate human developmental toxicity in the special population (e.g., pregnant women, infants) exposed to the pollutants. The results showed that the developmental toxicity was controlled by the main effects of the 10 pollutants. Among the 10 pollutants with developmental toxicity, the most significant pollutant with the main effects was PBB-153 (37.06%). In addition, the correlation and sensitivity analyses of the molecular developmental toxicity of the pollutants and structural parameters showed that the total energy value and infrared C–H vibration frequency of the pollutants were significantly correlated with human developmental toxicity. Accordingly, 15 supplementary food cofactors were selected for the Taguchi experiment design, among which the top seven cofactors were designed by fractional factorial analysis. The most significant cofactor that alleviated the developmental toxicity of PBB-153 exposure was the combination of carotene and docosahexaenoic acid (DHA), with an improvement of 17.28%. The combination of carotene and DHA significantly alleviated the effects of toxicity caused by most of the other pollutants, indicating that the selected supplementary food has certain universality. In this study, we developed a method to identify the characteristics of the developmental toxicity of pollutant exposure and developmental toxicity alleviation. Our study provided theoretical support for the regulation strategy of developmental toxicity caused by pollutants such as PBBs.

A Factorial Ecological-Extended Physical Input-Output Model for Identifying Optimal Urban Solid Waste Path in Fujian Province, China

Effective management of an urban solid waste system (USWS) is crucial for balancing the tradeoff between economic development and environment protection. A factorial ecological-extended physical input-output model (FE-PIOM) was developed for identifying an optimal urban solid waste path in an USWS. The FE-PIOM integrates physical input-output model (PIOM), ecological network analysis (ENA), and fractional factorial analysis (FFA) into a general framework. The FE-PIOM can analyze waste production flows and ecological relationships among sectors, quantify key factor interactions on USWS performance, and finally provide a sound waste production control path. The FE-PIOM is applied to managing the USWS of Fujian Province in China. The major findings are: (i) waste is mainly generated from primary manufacturing (PM) and advanced manufacturing (AM), accounting for 30% and 38% of the total amount; (ii) AM is the biggest sector that controls the productions of other sectors (weight is from 35% to 50%); (iii) the USWS is mutualistic, where direct consumption coefficients of AM and PM are key factors that have negative effects on solid waste production intensity; (iv) the commodity consumption of AM and PM from other sectors, as well as economic activities of CON, TRA and OTH, should both decrease by 20%, which would be beneficial to the sustainability of the USWS.

Redevelopment of Artificial Neural Networks for Predicting the Response of Bonded Concrete Overlays of Asphalt for use in a Faulting Prediction Model

Transverse joint faulting is a common distress in bonded concrete overlays of asphalt pavements (BCOAs), also known as whitetopping. However, to date, there is no predictive faulting model available for these structures. Therefore, the intended research is to develop a predictive faulting model for BCOAs. In addition, it is important to be able to account for conditions unique to BCOA when characterizing the response in a faulting prediction model. To address this, computational models were developed using a three-dimensional finite element program, ABAQUS, to accurately predict the response of these structures. These models account for different depths of joint activation, as well as full and partial bonding between the concrete overlay and existing asphalt pavement. The models were validated with falling weight deflectometer (FWD) data from existing field sections at the Minnesota Road Research Facility (MnROAD) as well as at the University of California Pavement Research Center (UCPRC). A fractional factorial analysis was executed using the computational models to generate a database to be used in the development of the predictive models. The predictive models, based on artificial neural networks (ANNs), are used to rapidly estimate the structural response at the joint in BCOA to environmental and traffic loads so that these responses can be incorporated into the design process. The structural response obtained using the ANNs is related to damage using the differential energy concept. Future work includes the implementation of the ANNs developed in this study into a faulting prediction model for designing BCOA.

Analysis of the Optimal Application of Blockchain-Based Smart Lockers in the Logistics Industry Based on FFD-SAGA and Grey Decision-Making

Blockchain technology has been applied to logistics tracking, but it is not cost-effective. The development of smart lockers has solved the problem of repeated distribution to improve logistics efficiency, thereby becoming a solution with convenience and privacy compared to the in-store purchase and pickup alternative. This study prioritized the key factors of smart lockers using a simulated annealing–genetic algorithm by fractional factorial design (FFD-SAGA) and grey relational analysis, and investigated the main users of smart lockers by grey multiple attribute decision analysis. The results show that the Web application programming interface (API) concatenation and money flow provider are the key success factors of smart lockers, and office workers are the main users of the lockers. Hence, how to better meet the needs of office workers will be an issue of concern for service providers.

Development of Analytical Method for Determination of Inorganic Constituents in Powder Refreshment using Dilute Mineral Acids and Detection by ICP OES

An analytical method for multi-elementary determination in powder refreshment, based on sample digestion using dilute mineral acids and detection by inductively plasma coupled optical emission spectrometry (ICP OES) is proposed. Chemometric tools, such as fractional factorial design and principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied to optimize the sample preparation conditions in closed block digester, and Doehlert design for spectrometer operation. Addition and recovery tests and analyses of certified reference material were performed to evaluate the precision and accuracy, and the results confirm the reliability of the proposed method. Limits of quantification (LOQ) between 0.02 and 36 μg g−1 were obtained. The analytical method was applied for determination of 24 inorganic constituents in 20 powder refreshment samples. Calcium, K, Mg, Na, P, S and Ti presented the highest concentrations. The analytical method was adequate for the determinations of inorganic constituents in powder refreshment samples by ICP OES.

Kinetic and isotherm modeling for acid blue 113 dye adsorption onto low-cost nutraceutical industrial fenugreek seed spent

The present study reports about the use of fenugreek seed spent as a new and efficient biosorbent for the removal of acid blue 113 dye from aqueous media and textile industrial effluent. The spent is a low-cost by-product of nutraceutical industry. The effects of various process parameters of adsorption, such as pH, initial dye concentration, adsorbent dose, adsorbent particle size, contact time and temperature onto nutraceutical industrial fenugreek seed spent (NIFGS) have been studied. Four numbers of two-parameter and six numbers of three-parameter isotherm models were used in the analysis of adsorption equilibrium data. Kinetic studies data conformed to pseudo-second-order model. Molecular diffusion studies were carried out using Weber–Morris, Dumwald–Wagner and film diffusion models. Change in enthalpy (ΔH°), entropy change (ΔS°) and Gibbs free energy change (ΔG°) of adsorption system indicated that the process is physisorption. Scanning electron microscopy, Fourier transform infrared spectroscopy and point of zero charge were used in characterizing the adsorbent. Fractional factorial experimental design and analysis of variance along with statistically developed model for adsorption helped to predict for a maximum adsorption of 661.5 mg g−1 using NIFGS. Application of NIFGS to textile industrial effluent and scaling up of the experimental process by three orders gave encouraging results.

Interactive effects of genotype, seasonality and extraction method on chemical compositions and yield of essential oil from rosemary (Rosmarinus officinalis L.)

Rosemary is an important crop for industrial production of essential oil. However, there is a significant effect of the genetic variation and the production process on the essential oil composition. Therefore, the present study was aimed at elucidating the effect of season and processing method on essential oil composition using the volatile structure of rosemary germplasm that was cultivated at the Newe Ya'ar research center for 30 years. Analysis of the essential oil composition of thirty-three germplasm identified four chemical groups: a '1,8-cineole' type, a 'verbenone' type, a 'camphor' type, and a 'α-pinene' type. Application of fractional factorial analysis of variance to the data indicated interactions of volatile by germplasm, volatile by method (hydro-distillation or solvent extraction), and volatile by season (summer 2017 or winter 2018), but not for germplasm by method, germplasm by season or method by season. This suggests that the germplasm was seasonally stable. Yet, the interaction between volatile by method pointed out that more camphor and 1,8-cineole were obtained by distillation, whereas more α-pinene was obtained by solvent extraction. Using four germplasm that were selected as chemo-type representatives for comparing hydro-distillation by Clevenger modified apparatus and steam-distillation, α-pinene was identified as a minor component in the hydrosol, whereas 1,8-cineole, camphor, borneol and verbenone were major components in the hydrosol. The outcome of the study reflects the importance of evaluating the effects of environment, genetic and processing methods on essential oil composition for accurate industrial breeding and production of essential oils.

Facile radiolabeling optimization process via design of experiments and an intelligent optimization algorithm: Application for omeprazole radioiodination.

The major uses of radiopharmaceuticals (RP) in clinical areas are diagnosis and/or therapy. The present study aimed to utilize the application of fractional factorial design analysis (FFDA) coupled with particle swarm optimization algorithm (PSO) to assess the optimization of RP production process. In this regard, omeprazole (OMP), which is gastric parietal cell proton pump inhibitor (PPI), was radiolabeled with iodine-125 (125 I) isotope in order to be used as a radiotracer for stomach imaging. Different factors that affect radiolabeling process were studied. According to the proposed design, just 16 experimental runs of radiolabeling process were performed using the extremes of each factor. In addition, one run was executed at the mean point of each factor. Undesirable maximum radiolabeling yield (RY) of radioiodinated omeprazole (125 I-OMP) was deduced from application of FFDA (88.4%). Furthermore, after applying PSO with changing limits of one factor, the maximum RY of 125 I-OMP was found to be 93.78%. Moreover, the practically verification from optimum conditions, which obtained from PSO, was found to give an RY of 93.99%. Overall, the findings of this study confirmed the potential use of that hybrid design for optimization of radiolabeling processes.

Sensitivity analysis and multi-criteria optimization of SMA cable restrainers for longitudinal seismic protection of isolated simply supported highway bridges

Considering the flexibility of isolation bearings, isolated bridges are susceptible to unseating problems or even collapse due to excessive displacement under strong earthquakes. Shape memory alloy (SMA) wires can be utilized as seismic restrainers to confine this displacement. Recently, a design procedure for SMA restrainers has been proposed based on a linearized two degree-of-freedom analytical model. The key design parameters include three geometric factors (the ratio of the allowable displacement to the seat width, Δa/Lsw, horizontal angle, θ0, and normalized elongation parameter α), and three factors related to material properties of SMA (the maximum applied strain, εmax, the elastic modulus, E0, and the austenite to martensite start stress, fy,SMA). The objective of this study is to optimize the design procedure by evaluating the effect of each design factor on the seismic response of an isolated bridge which is retrofitted by SMA restrainers. After conducting a one-half fractional factorial analysis, it was found that the material factors have slight impact on the relative displacement between the pier and the girder, and the base shear of the pier, with percentage contributions smaller than 4% and 13%, respectively. Then, a full factorial analysis is followed through a multi-criteria decision making process in order to optimize the SMA restrainer. The results show that the optimal value of Δa/Lsw, which effectively reduces the relative displacement and simultaneously control the base shear of the piers, is 0.4; the optimum value of θ0 ranges from 0 to π/4; and the most effective value of α is 1.0.

Fractional Factorial Analysis for Identifying Significant Factors in Extraction of Jacalin From Jackfruit Seeds Using Anionic Reverse Micellar System

Abstract Reverse micellar extraction is emerging as one of the most reliable method for extraction and purification of various proteins and enzymes. In this study, fractional factorial design was used to identify the significant factors having the largest effects on the forward extraction process of jacalin using reverse micelles formed by an anionic surfactant, sodium bis(2-ethylhexyl) sulfosuccinate (AOT). Effect of five different factors, namely the aqueous phase pH, surfactant (AOT) concentration, salt (NaCl) concentration, contact time and volume ratio of organic to aqueous phase on the forward extraction efficiency were investigated using the 25-1 fractional factorial design. The results showed that only the aqueous phase pH, NaCl concentration and concentration of AOT had significant effects on the forward extraction efficiency of jacalin into AOT reverse micelles. Results obtained indicated that the fractional factorial design was a useful method for screening many process factors with minimum number of experiments.

Effect of the engine calibration parameters on gasoline partially premixed combustion performance and emissions compared to conventional diesel combustion in a light-duty Euro 6 engine

A design of experiments analysis was performed to investigate the effects of pilot quantity, combustion phasing and exhaust gas recirculation on performance and emissions in a gasoline partially premixed combustion to find out the optimal combination of the all varied parameters.The experimental activities were performed on a light-duty Volvo Euro 6 diesel engine. The test was performed under steady-state operating conditions, nine test points were chosen inside the operating area of the New European Driving Cycle and the Worldwide Harmonized Light vehicles Test Cycles. A fractional factorial analysis in partially premixed combustion on the single and combined effect of the main engine calibration parameters and a global comparison between partially premixed combustion and conventional diesel combustion on the engine performance and emissions adopting the optimal calibration parameters obtained from design of experiments analysis for both combustion modes analysed were presented. The purpose was to obtain the calibration parameters setting that permits to achieve high efficiency and low emissions as well.The partially premixed combustion results show the highest efficiency and lowest NOx emissions adopting a high exhaust gas recirculation rate combined with advanced combustion phasing and lower pilot quantity. Higher efficiency, up to 2.0% units, was obtained in partially premixed combustion with respect to the conventional diesel combustion due to the lower heat transfer loss. Lower soot (about two times) and NOx (about −0.5 g/kWh) levels with partially premixed combustion were obtained and compared to conventional diesel combustion at the same exhaust gas recirculation level.A reduction of about 5% of CO2 and fuel consumption with a 50% of reduction on NOx and soot simultaneously were obtained for partially premixed combustion on the New European Driving Cycle estimation results with respect to the diesel combustion. The information derived from this work are useful to develop and calibrate a light-duty engine that operate in gasoline partially premixed combustion mode achieving NOx close to the Euro 6 limit without adopting any after treatment system.

Impact of Human Actions on Building Energy Performance: A Case Study in the United Arab Emirates (UAE)

There is a growing interest in the literature to understand how actions taken by occupants and facility managers can affect building performance. However, user-centric building energy research: (1) remains understudied compared to design-focused research efforts; (2) overlooks combined effects or uncertainty in multiple parameters; and (3) typically does not cover particular types of buildings (e.g., educational facilities), nor buildings subject to extreme weather conditions. This paper fills an important gap in the literature by proposing a comprehensive energy modeling and analysis framework to quantify the impact of human action on building energy consumption. The framework applies various data analysis methods such as differential, fractional factorial, and Monte Carlo analysis methods, in order to capture potential combined or synergetic effects of human actions on building performance. A case study is then presented on typical educational buildings located in the extreme hot climate of Abu Dhabi, United Arab Emirates (UAE). Results indicate that uncertainty in human actions can lead up to a ±25% variation from average energy consumption levels, confirming the significant role that people have in making their built environment more efficient and sustainable.

A Novel Sustainable Design to Develop Polypropylene and Unsaturated Polyester Resin Polymer Composites From Waste of Major Polluting Industries and Investigation on Their Physicomechanical and Wear Properties

A novel concept of using Nutraceutical Industrial Coriander Seed Spent (NICSS) and Congo red (CR) dye adsorbed onto NICSS (CR‐NICSS) as filler materials to fabricate polypropylene (PP) green thermoplastic composites and unsaturated polyester resin unsaturated polyester resin (USP)/CR‐NICSS and USP/NICSS composites is presented. To maximize the adsorption of CR on NICSS and to study the influence of five parameters viz, process time, temperature, initial concentration of dye, adsorbent dosage and pH of the reaction mixture, we use a two‐level fractional factorial design approach and analysis of variance to develop statistical model. The composites were evaluated for physico‐mechanical and tribological properties and compared with the thermoplastic composites fabricated using NICSS. Flexural strength and flexural modulus of composites were improved by adding CR‐NICSS and NICSS into PP matrix. The abrasive wear behavior, wear volume loss and specific wear rate as a function of abrading distance at 150, 300, 450, and 600 m and different loads of 23.54 and 33.54 N at 200 rpm were determined. The water absorption characteristics of thermoplastic composites were studied. The surface morphology of tensile fractured PP/CR‐NICSS was examined under scanning electron microscope. The influence of water and thermal ageing on tensile strength and physical properties, such as density, surface hardness and effect of chemicals on USP/CR‐NICSS and USP/NICSS are reported. POLYM. COMPOS., 40:1142–1157, 2019. © 2018 Society of Plastics Engineers

Cerebrospinal Fluid Pressure: Revisiting Factors Influencing Optic Nerve Head Biomechanics.

PurposeTo model the sensitivity of the optic nerve head (ONH) biomechanical environment to acute variations in IOP, cerebrospinal fluid pressure (CSFP), and central retinal artery blood pressure (BP).MethodsWe extended a previously published numerical model of the ONH to include 24 factors representing tissue anatomy and mechanical properties, all three pressures, and constraints on the optic nerve (CON). A total of 8340 models were studied to predict factor influences on 98 responses in a two-step process: a fractional factorial screening analysis to identify the 16 most influential factors, followed by a response surface methodology to predict factor effects in detail.ResultsThe six most influential factors were, in order: IOP, CON, moduli of the sclera, lamina cribrosa (LC) and dura, and CSFP. IOP and CSFP affected different aspects of ONH biomechanics. The strongest influence of CSFP, more than twice that of IOP, was on the rotation of the peripapillary sclera. CSFP had similar influence on LC stretch and compression to moduli of sclera and LC. On some ONHs, CSFP caused large retrolamina deformations and subarachnoid expansion. CON had a strong influence on LC displacement. BP overall influence was 633 times smaller than that of IOP.ConclusionsModels predict that IOP and CSFP are the top and sixth most influential factors on ONH biomechanics. Different IOP and CSFP effects suggest that translaminar pressure difference may not be a good parameter to predict biomechanics-related glaucomatous neuropathy. CON may drastically affect the responses relating to gross ONH geometry and should be determined experimentally.

Application of Conjoint Analysis to Improve Reliability of Dietician Consultation in Pediatric Celiac Disease.

Celiac disease (CD) management involves lifelong adherence to a gluten-free diet, making the dietician a key member in CD care. However, our institution lacked a standardized process for dietary consultation in newly diagnosed CD. To understand provider CD care preferences, a 24-1 fractional factorial conjoint analysis was performed. Attributes studied (2 levels each) included type of initial follow-up gastroenterology (GI) provider, interval from diagnosis to follow-up, concurrence of initial dietary consultation with gastroenterology visit, and on-going follow-up GI provider. CD care was standardized in July 2014 to facilitate concurrent visits with the clinician and dietician during the same clinical session. Changes to mean time of dietary consultation and reliability of dietary consultation were monitored using an individual-control and G-control chart, respectively. Standard control chart rules were followed. Conjoint analysis identified shorter time to initial follow-up visit and concurrent GI/dietician visits as more important attributes in newly diagnosed CD subjects' care. Types of follow-up provider during first or subsequent visits were identified as less important attributes. After initiation of a standardized follow-up process, a special cause was identified in December 2015 with a decrease in the mean time to dietary consultation from 30 to 20 days. In addition, standardized follow-up resulted in a more reliable process as evident by a special cause on the G-control chart in February 2015. Conjoint analysis identified attributes thought to be important in CD follow-up care. After redesign of our care process, a decrease in time to dietary consultation with improved reliability was observed.

Factor screening experiments using fractional factorial split plot designs and regression analysis in developing a top-down nanomanufacturing system for recycling of welding rod residuals

A promising technology for integration of top-down nanomanufacturing is equal channel angular pressing, a process transforming metallic materials into nanostructured or ultra-fine grained materials with significantly enhanced performance characteristics. To bridge the gap between process potential and actual manufacturing output a novel prototype system identified as indexing equal channel angular pressing (IX-ECAP) was developed to capitalize on sustainable engineering opportunities of transforming spent or scrap engineering elements into key engineering commodities by recycling 4043 aluminium alloy welding rod residuals. A resolution III fractional factorial split-plot experiment assessed significance of predictors on the response, microhardness, with multiple linear regression used for model development. Five process parameters involving pressing temperature, number of passes, pressing speed, back pressure, and vibration were studied. Microhardness conversions allowed theoretical determination of grain sizes employing Hall-Petch relationships. IX-ECAP offered a viable solution whereby processing of discrete variable length work pieces proved very successful.

A FACTORIAL ANALYSIS STUDY ON FACTORS CONTRIBUTION TO FERULIC ACID PRODUCTION FROM OIL PALM FROND WASTE

Enzymatic hydrolysis of fibre-pressed oil palm frond (FPOPF) was done using mix culture for the production of ferulic acid. Mix culture was prepared by acclimatizing the soil obtained from palm oil plantation with FPOPF substrate for 30 days. The substrate had a ratio of FPOPF to water of 1:10. Design Expert 7.1 was used to aid the experimental design. A half fractional two-level factorial analysis with five factors was selected for the experimental design resulting in a total of 16 runs. The factors controlled were temperature (A; 26°C and 40°C), pH value (B; 5 and 9), agitation speed (C; 0 and 150 rpm), inoculum percentage (D; 2% and 10%), and response time (E; 1 and 3 days). The result obtained showed that the experimental design model was significant with a coefficient of determination value of 0.8978. Two factors that contributed the most to the process were temperature and pH value. This model was proved to be repeatable with a validation test percentage error at 4.15% to 6.83%.