Modeling and optimization of developed cocoa beans extractor parameters using box behnken design and artificial neural network

doi:10.1016/j.compag.2020.105715

Computers and Electronics in Agriculture

Volume 177, October 2020, 105715

https://doi.org/10.1016/j.compag.2020.105715 Get rights and content

Highlights

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The designed cocoa been extractor consists of hopper, metal rollers, chute, rotating cylindrical strainers, frame, prime mover, and pulleys.
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The roller speed, cocoa pod size and inclination angle of strainer were optimized by box-behnken design (BBD) and artificial neural network (ANN) for breaking cocoa pods and separations of beans.
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During optimization of machine performance ANN represents better prediction of experimental data over BBD.
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The optimized cocoa beans extractor has a maximum capacity, efficiency and low energy requirement.

Abstract

Breaking the cocoa pod and extracting the beans is a strenuous and laborious task; hence an attempt was made to design and optimize a mechanized continuous cocoa beans extractor. Cocoa fruit was fed manually into a breaker unit through the hopper. The tangential force of the roller pushed the cocoa pod towards the gap resulted in breakage. The strainer attached with pod breaker separate the cocoa kernels from cocoa pod and placenta. The developed machine was tested and the parameters have been optimized by using box-behnken design (BBD) and artificial neural network (ANN). The input variables of bod breaker were roller speed (260, 360, and 460 rpm), cocoa pod size (140, 160, and 180 mm) and inclination angle of strainer (40, 45, and 50°) and out put variables involving capacity, machine efficiency, energy requirement, shelling efficiency, beans separation efficiency, and bean damage percentage.. The coefficient of determination (R2) of BBD ranged between 0.95 and 0.99, and the sum of squared error (SSE) ranged between 8.38 and 158.3, Whereas the R² of ANN ranged between 0.94 and 0.99, and mean squared error (MSE) ranged between 0.0013 and 0.0074. This indicates ANN design superior performance over BBD. The performance parameters of 360 rpm roller speed, 160 mm pod size and 45° inclination angle were determined as optimum conditions to obtain maximum capacity (626.8 kg/h), machine efficiency (96.78%), shelling efficiency (96.3%), beans separation efficiency (86.42%), and low energy requirement (10.54 kJ) with less beans damage percentage (<1%).

Introduction

Cocoa is an excellent primary material for the manufacture of chocolates, cosmetics, health drinks, pharmaceuticals, etc. It contains about 50 percent fat, which is useful for producing a candle, ointments, pharmaceutical products, cosmetics, etc. (Porter, 2006, Prosapio and Norton, 2019). The cocoa pod is the best source for the creation of potash manure, soap, biogas, and speck boards (Babayemi et al., 2010, Adjin-Tetteh et al., 2018). The global needs for cocoa beans have been steadily increased over recent decades due to the increased consumption of chocolate and chocolate-flavored products (Sunoj et al., 2016).

At present, the cocoa pods have been broken manually using wood and machete. Thus, it is a strenuous and time-consuming process. The manual method of cocoa pod breaking leads to damages to the beans, increase the percentage of bean losses, and reduces the profit. The strenuous task causes the persistent weakness and sickness of the labor and farmer, resulting in a low standard of health (Huang et al., 2010, Patrício and Rieder, 2018).

The extensive requirement of labour during the manual method results in the high cost of production. Adewumi and Fatusin, 2006, Chamsing et al., 2006 have been developed a hand operated mechanical cocoa pod breaker. The automated method of extracting the cocoa beans will reduce fatigue for the farmers and the labours. Also, the losses usually occur during the manual breaking of the pods and separating beans from the placenta with wood, and cutlass will be reduced, which enhances income for the farmers. The time required for extracting the beans using the manual method will be reduced by the mechanical way (Kate et al., 2018).

The optimization of machine design conditions becomes a problem which has been vigorously analyzed as concern agriculture, biosystems, and food processing industries pertained different devices and abilities were applied for this view in order to accomplish reasonable quality solutions (Sudha et al., 2016, Le Chau et al., 2019). BBD from Response Surface Methodology (RSM) can be depicted as an empirical modeling system and could be utilized for developing, improving, and optimizing complex processes (Madadlou et al., 2009, Chau et al., 2018, Chau et al., 2019). RSM has the advantage of decreasing the count of experimental runs, which is enough to furnish statistically acceptable results (Nourbakhsh et al., 2014). RSM used to optimize agricultural oriented machine design (Saldaña-Robles et al., 2020), post-harvest processing machine design (Sofu et al., 2016), food processing machine designs (Umani et al., 2019), dairy processing machine design (Madadlou et al., 2009), food product development optimization (Kothakota et al., 2013a, Kothakota et al., 2013b, Kothakota et al., 2016, Kothakota et al., 2017, Nagpal et al., 2018, Pandiselvam et al., 2019a), food process optimization (Santana et al., 2018, Sagarika et al., 2018, Shameena Beegum et al., 2019) and food preservation optimization (Nourbakhsh et al., 2014, Gaurh et al., 2017). Artificial neural network (ANN) have great learning capacity and potential of determining and modeling the complicated non-linear relationship among the input and the output of a system in comparison to RSM. ANN has appeared as an extra robust and excellent modeling tool, as a result of its ability to determine from observations and to draw conclusions using rationalization and predictive modeling from the behaviour of complex nonlinear method. Numerous investigations had stated on ANN applications for design and performance of the machine in agricultural processing (Saldaña-Robles et al., 2020), post-harvest processing of crops (Nourbakhsh et al., 2014, Santana et al., 2018), food storage (Sanaeifar et al., 2018), food preservation (Santana et al., 2018), dairy processing (Madadlou et al., 2009) and process optimization for product development (Nagpal et al., 2018, Pandiselvam et al., 2019a).

Considering the aforementioned factors, the post-harvest processing of cocoa has to be mechanized.. Hence, this present study aimed for 1) design and development of continuous cocoa beans extractor. 2) modeling and optimizing the machine performance parameters by applying BBD and RSM 3) cost analysis and field testing of a developed prototype in comparision with the manual process.

Section snippets

Raw material

Matured cocoa fruits (Theobroma cacao L.) of two verities (Criollo and Forastero) were procured from M/s Cadbury unit of Kerala Agricultural University, Thrissur, India, and the progressive farmer from Karuvarakundu, Malappuram, India. The cocoa pods were cleaned and sorted from cracked, skin injuries, and disease affected pods. Moisture content of cocoa pod husk was determined by AOAC (2000) method. The experiment was carried out at fixed moisture 81.21 ± 1% wb (for Criollo veriety) and

Experimental design

The performance evaluation and experimental optimization from a designed prototype machine (Fig. 1) have been executed through the Box-behnken design (BBD) and artificial neural network (ANN).

Engineering properties of cocoa pod

The engineering properties of cocoa pods could be useful for development of pod breaking machine. The physical properties of cocoa pod such as lenth (124–188 mm), diameter (73–97 mm), sphericity (0.62–0.70), volume (293–785 cm³), bulk density (384–417 kg/m³) and porosity (47–55%), were used to design the hopper. The frictional properties (SS-0.29 to 0.33, AS-0.32 to 0.34, GI-0.34 to 0.40 and PW- 0.33 to 0.39) on different materials would be useful for fabrication material selection and

Conclusion

The present study concentrated on the design and development of continuous cocoa bean extractor. During design, the outcome from the operation valuation disclosed that the shape of the pod, roller speed, and inclination of the strainer of the machine had more impact on machine performance pursed by roller clearance. BBD and ANN model have been applied for modeling and optimizing machine performance conditions. BBD and ANN regression coefficients have been found to maximum 0.994 and 0.999,

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

The financial support received from the Department of Food and Agricultural Process Engineering, Kerala Agricultural University, is gratefully acknowledged.

References (47)

M. Adjin-Tetteh et al.
Thermochemical conversion and characterization of cocoa pod husk a potential agricultural waste from Ghana
Ind. Crops Prod.
(2018)
I.M. Gana et al.
Development and testing of an automated grain drinks processing machine
Food Bioprod Process
(2017)
Y. Huang et al.
Development of soft computing and applications in agricultural and biological engineering
Comput. Electron. Agric.
(2010)
M.A. Koyuncu et al.
Cracking characteristics of walnut
Biosyst. Eng.
(2004)
A. Madadlou et al.
Response surface optimization of an artificial neural network for predicting the size of re-assembled casein micelles
Comput. Electron. Agric.
(2009)
M. Nagpal et al.
Ultrasound, microwave and Box-Behnken Design amalgamation offered superior yield of gum from Abelmoschus esculentus: Electrical, chemical and functional peculiarity
Comput. Electron. Agric.
(2018)
H. Nourbakhsh et al.
Prediction of red plum juice permeate flux during membrane processing with ANN optimized using RSM
Comput. Electron. Agric.
(2014)
D.I. Patrício et al.
Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review
Comput. Electron. Agric.
(2018)
R.C. Pradhan et al.
Design, development and testing of hand-operated decorticator for Jatropha fruit
Appl. Energy
(2010)
V. Prosapio et al.
Development of fat-reduced chocolate by using water-in-cocoa butter emulsions
J. Food Eng.
(2019)

C.N. Okoye et al.

Design and development of secondary controlled industrial palm kernel nut vegetable oil expeller plant for energy saving and recuperation

J. Food Eng.

(2008)

A.L. Saldaña-Robles et al.

Structural design of an agricultural backhoe using TA, FEA, RSM and ANN

Comput. Electron. Agric.

(2020)

A. Sanaeifar et al.

Fusion of dielectric spectroscopy and computer vision for quality characterization of olive oil during storage

Comput. Electron. Agric.

(2018)

J.C.C. Santana et al.

Optimization of vacuum cooling treatment of postharvest broccoli using response surface methodology combined with genetic algorithm technique

Comput. Electron. Agric.

(2018)

V. Sharma et al.

Evaluation of a centrifugal impaction-type decorticator for shelling tung fruits

Ind. Crops Prod.

(2013)

M.M. Sofu et al.

Design of an automatic apple sorting system using machine vision

Comput. Electron. Agric.

(2016)

L. Sudha et al.

Optimization of process parameters in feed manufacturing using artificial neural network

Comput. Electron. Agric.

(2016)

S. Sunoj et al.

Nondestructive determination of cocoa bean quality using FT-NIR spectroscopy

Comput. Electron. Agric.

(2016)

K.C. Umani et al.

Development and testing of an automated contact plate unit for a cassava grater

Comput. Electron. Agric.

(2019)

R.K. Vishwakarma et al.

Novel method and machine for dehulling of guar seeds and optimisation of dehulling process

Food Bioprod. Process.

(2016)

B.A. Adewumi et al.

Design, fabrication and testing of an impact-type hand operated cocoa pod breaker

Int. J. Agric. Eng.CIGR J.

(2006)

S.A. Aliu et al.

Development and performance evaluation of a cocoa pod breaking machine

J. Adv. Mater. Res.

(2012)

AOAC

Official Methods of Analysis

(2000)

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Published by Elsevier B.V.

Modeling and optimization of developed cocoa beans extractor parameters using box behnken design and artificial neural network

Highlights

Abstract

Introduction

Section snippets

Raw material

Experimental design

Engineering properties of cocoa pod

Conclusion

Declaration of Competing Interest

Acknowledgment

Ind. Crops Prod.

Food Bioprod Process

Comput. Electron. Agric.

Biosyst. Eng.

Comput. Electron. Agric.

Comput. Electron. Agric.

Comput. Electron. Agric.

Comput. Electron. Agric.

Appl. Energy

J. Food Eng.

J. Food Eng.

Comput. Electron. Agric.

Comput. Electron. Agric.

Comput. Electron. Agric.

Ind. Crops Prod.

Comput. Electron. Agric.

Comput. Electron. Agric.

Comput. Electron. Agric.

Comput. Electron. Agric.

Food Bioprod. Process.

Design, fabrication and testing of an impact-type hand operated cocoa pod breaker

Int. J. Agric. Eng.CIGR J.

Development and performance evaluation of a cocoa pod breaking machine

J. Adv. Mater. Res.

Official Methods of Analysis