optimization of operation of a pyrolysis reactor of

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OPTIMIZATION OF OPERATION OF A PYROLYSIS REACTOR OF INDUSTRIAL SOLID WASTES Ana Rosa Costa-Muniz1*, Ariovaldo Bolzan2, Luis Antonio Rezende Muniz1 1

Departamento de Engenharia Química - Universidade de Caxias do Sul Departamento de Engenharia Química e Engenharia de Alimentos - Universidade Federal de Santa Catarina

2

Abstract. In this work, a case study of recycling of polymeric wastes through pyrolysis is carried out. These wastes were originated from companies of the South of Brazil and are considered critical regarding their generated volume and harmfulness degree. The studied wastes were: Polypropylene(PP), Acrylonitrile Butadiene Styrene(ABS) and paint sludge waste. In this work it was developed a methodology for the optimization of a multiproduct pyrolysis reactor operation, where the biggest challenges are: to generate nontoxic products and insert them into the market with competitive price, to return the invested capital to develop, to assemble and to operate the pyrolysis plant, as well as, to utilize a viable method of optimization for a batch multiproduct production system. For that, this work is composed by three main parts: a) online data acquisition through an experimental system assembled in bench-scale; b) gathering information, in field, about the region where the pyrolysis plant is to be inserted; and c) development of an optimization software. The optimization of the multiproduct pyrolysis plant consisted in the actualization, in a month, of the production sequencing and of the operational set points that are needed to reach the objectives of the production planning, through the maximization of a profit function. This function involved economical and technical data such as, temperature and batch time, prices and, processed waste and produced oil amounts, consumed and generated amounts of combustible gases as well as restrictions on the raw material and product’s demand. This optimization problem was solved through a solution methodology using Genetic Algorithms. The result is a flexible computational code that permits rescheduling to accommodate today’s fast changes in market requirements, production capacities and operational conditions.

Keywords: pyrolysis, production planning and optimization.

1. Introduction In the last years, Brazil has produced approximately 3 millions tons of harmful industrial waste per year, however only 22% is treated adequately. The South and Southeast regions have the main states that generate such waste, according to the website of the Ministry of Environment. In the South region, the Rio Grande do Sul state generates circa 300.000 tons of industrial solid wastes and 63,6% are considered harmful. Caxias do Sul city has been chosen as the study region of this work for being the third bigger metal-mechanic pole in Brazil. The city has 5.865 industries that generate a high volume of industrial waste of plastic and paint, not only for the generated volume but also for its degree of perilousness. Nowadays, the two main destination of these wastes are: incineration and industrial landfills, which cause serious environmental and social risks, mainly due to the high volume of gas generated in the incineration process and the increasing proximity of landfills to the city. An alternative for the rationalisation of the use of such polymeric wastes through pyrolysis was utilised in this work. Pyrolysis is a thermal decomposition in the absence or with a minimum of oxygen with simultaneous generation of gases and liquids. These products can be used to supply energy to the process or can be commercialised as chemicals or fuel. A detailed study, involving technical and economical viability was carried out as well as the production planning and suitable supervision of a multiproduct pyrolysis reactor. The utilisation of genetic * Ana

Rosa Costa Muniz. Address: Departamento de Engenharia Química - Centro de Ciências Exatas e Tecnologia, Universidade de Caxias do Sul - Bl.G, C.P.1352- CEP 95001-970 - Caxias do Sul – RS - Brazil E-mail: [email protected]

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algorithms as the solution strategy was due to its success as a method of operational research in several problems of large scale, and also due to its capacity of simultaneous determination of production planning, scheduling and operational conditions, besides the scientific curiosity in using the fundamentals of genetics with the evolution theory as an alternative for the solution of an environmental issue.

2. Literature review Technologies of waste treatment for energy recovery represent an interesting alternative in treatment of industrial solid wastes. Incineration is the method most used, but the high costs for pollution control of incinerators as well as chemical composition of several polymers, especially those containing chlorine, are demanding alternative treatments. Bockhorn (1999) defined pyrolysis as a procedure compared to the incineration and it has as main advantages the economy on gas washing and absence of several oxygenated toxic compounds. However, it is a fact that several polymers need a considerate amount of energy to break up the molecules. Caputo and Pelagagge (2002a) showed that only plants with direct utilisation of the generated fuels are economically viable. Germany and Japan have the biggest industrial pyrolysis plants for the conversion of wastes into energy, fuel and gas with different types of feeding: packing materials, woodworking wastes, wastes from varnish factories (Meneghetti et al., 2002), municipal solid waste (Caputo and Pelagagge, 2002b) and other wastes with high calorific power (Bébar et al., 2002). The processing of several types of wastes makes the pyrolysis to be compared to a multiproduct plant in batch mode, where these several wastes can be pyrolysed in the same equipment. The modelling of a multiproduct batch plant integrates several process operations in a complex way, typically planning, scheduling, supervision and data acquisition. The best way for integration is reached through a formulation and solution of mathematical models appropriately structured. Most of these models consider a hierarchical decomposition scheme where the planning problem is represented by a model that adjusts the production objectives to maximize profit and, in the lower level, the scheduling problem is reduced to a sequence of sub problems that have to reach the objectives adjusted by the planning problem. The integration of the two levels is performed by a heuristic method (Birewar and Grossmann, 1990, Susara and Grossmann, 2003 and Kin et al., 2003). In this work, the integration is determined through a solution strategy that uses genetic algorithms that solve simultaneously planning, scheduling and supervision problems of a multiproduct pyrolysis batch reactor, generating always-viable solutions. This is a large scale problem and difficult to solve due to the complexity of the involved chemical reactions as well as restrictions to demand and time horizon involved during the search for the optimum solution, making inadequate the use of conventional optimization methods. Several research works that have been developed in this area are limited to the study of scheduling of flow shop and job shop machines, where genetic algorithms have shown great solution potentiality in an effective and efficient way. (Srikanth and Barkha, 2004, Jensen et al., 2003, Wang et al., 2000).

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3. Methodology The first step of the optimization process of a pyrolysis plant of industrial solid waste is the characterisation of the region to place the plant, aiming to determine the type of waste to be processed, average generation rate and equipments capacity. After studying the fundamentals of such equipments, the gas and liquid pyrolytic products were characterised and a new research was performed in the study region to determine the demand and economic value of these products. The economic potential of the region for those products was confirmed then an experimental apparatus was set to collect temperature and batch time data, electrical power consumed by the reaction and mass and composition of the resulting products of pyrolysis of three types of industrial solid waste. These variables were utilised for modelling the product yield, energy consumption and energy produced by the reaction and these variables were also used to classify the obtained oils. These models were incorporated to the multiproduct batch plant, integrating production planning, scheduling and supervision. With the maximum profit determination, an economic analysis was carried out through the determination of the annual rate of income over the investment. Figure 1 shows a flowchart of the utilised methodology. Characterisation of the study region Rate of generation of wastes

Type of waste

Products characterisation

Products are viable Rate of demand of wastes

Y Online data acquisition

N

oils gases

Exit xj Epi Eci

Price of products

Experimental data adjustment Optimisation

ROI

Economic analysis

Multiproduct batch plant modelling Production planning Scheduling production Operational conditions Profit

Fig. 1. Flowchart of the utilised methodology 3.1. Characterisation of the study region A wide bibliographic research was performed in loco on the characteristics of the region where the pyrolysis plant is intended to be built. This step has the participation of several governmental organs as well as universities and public and private companies. It was possible to determine with this study the types of wastes to study as a function of the generated volume and its degree of perilousness, as well as to determine the average rate of generation of these wastes, which is needed to determine the capacity of an industrial solid waste pyrolysis plant.

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3.2. Products characterisation Experimental data were collected through an experimental apparatus consisting of a carbon steel reactor with capacity of 8.000mL, internal diameter 167mm, height 280mm and thickness 20mm, a 304 stainless steel flash separator with capacity of 5.000mL, with internal copper serpentine for cooling and a data acquisition system. Chemical characterisation of the produced gases and oils was carried out by a flame ionisation chromatograph, (GC-HP 6890, model MST – 5973) coupled with a mass spectrometer and the pyrolysis oils were classified according to the Agência Nacional do Petróleo (Brazilian National Petroleum Agency) norms, through the following equipments: pycnometer for density measurement, calorimeter (Babelsberg) to measure the inferior and superior calorific power, viscosimeter (Brookfield DV++m model LV) and an equipment to measure the flash point through the Pensky-Martens method.

3.3. Online data acquisition The system for data acquisition comprises of a type J thermocouple linked to a signal receptor, which transforms the output signal of the thermocouple to millivolts on the range of 0 to 5 volts, and to a data acquisition board (Computer Boards model CIO-DAS-Jr 333). A power block was utilised to transform the signal from the board from 0 to 5 volts to the tension of 0 to 220 volts and it was connected to an electrical resistor that receives the signal and uses it to heat the reactor until the desired temperature. This system allows collecting temperature data, batch time and electrical tension. 3.4. Experimental data adjustment Collected data for temperature and batch time, electrical tension consumed by the reaction, mass and composition of the resultant pyrolysis products were utilised for mathematically modelling the products yield, consumption and energy production of the reaction. All adjustments were performed using the software “Statistica” with the Quasi-Newton optimization method, having as the objective function the difference between predicted values and square observed values with a convergence criterion less than 0.0001. 3.5. Multiproduct batch plant modelling The utilised production-planning model for the multiproduct pyrolysis plant is shown by Equations (1) to (9), with the following characteristics: a) Income is obtained through products selling as well as raw material acquisition, once the companies pay to discharge their waste; b) Oils produced by pyrolysis and gas remaining from its utilisation in generating energy for the system are considered the selling products; c) 80% of manufacturing costs were considered to be the energetic ones because they represent the most significant production costs. The remaining 20% include the following costs: maintenance, manpower, distribution and selling. This restriction is shown by Eq. (7) and d) Setup time considers loading and unloading the reactor and is the same for all wastes.

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T  R P Maximize ∑  ∑ QRit PRit + ∑ QP jt PP jt − GLt C t − 0.2CTPt t =1  i =1 j =1

  

(1)

Subject to: Tmini ≤ TRi ≤ Tmaxi

i = 1,..,R

(2)

tmini ≤ tRi ≤ tmaxi

i = 1,..,R

(3)

QRit ≤ Omaxit

i = 1,..,R

t = 1,....,T

(4)

QPjt ≤ Dmaxjt

j = 1,..,P

t = 1,....,T

(5)

R GLt = ∑ (GC it − GPit ) i =1

i = 1,..,R

t = 1,....,T

(6)

t = 1,....,T

(7)

T

CTPt = 1,25 ∑ C t t =1

R Nbit ∑ ∑ (t i )k ≤ H t i =1 k =1

i = 1,..,R

t = 1,....,T

(8)

QRit, QPjt, GPit, GCit, CTPt,TRi, tRi, ti ≥ 0

i = 1,..,R

t = 1,....,T

(9)

Eq. (1) shows the maximisation of the profit function, which is the difference between income and manufacturing costs. Eq. (2), (3), (4) and (5) are restrictions that limit the values of temperature, batch time and processed waste amounts and produced oil amounts, respectively. Eq. (5) expresses the liquid mass of combustible gas in period t, which is the difference between the total mass of produced and consumed combustible gas in period t. Eq. (8) is a restriction of time horizon that limits the time spent for processing all wastes during the time t and Eq. (9) shows the non- negativity restrictions. 3.6. Optimization The strategy to insert the scheduling and supervision problems into the planning problem was done utilising an optimization method based in genetic algorithms. In the method utilised, every population string is formed by the following sequence of variables: type of waste, temperature and processing time, and the string length is determined by restrictions of products demand, wastes availability and time, Eq. (4), (5) and (8). A fitness value was attributed for each string, which is the same of the objective function, associated to the profit. Scaling was utilised in the beginning of optimization, aiming to keep the competitiveness level amongst the strings. Two strings from the population, with probability associated to the value of their fitness, were random selected. It means, as higher is the profit, higher is the probability of selection (roulette). Before crossover, all the restrictions of the model were tested, avoiding useless calculation. Crossover was performed amongst the same genes or variables in their different parents, with probability of a pair of selected individuals are crossover, being dependent on the crossover rate. The mutation operator was applied through a draw for each gene, with probability of gene mutation, being dependent on the mutation rate. The operator elitism was also utilised, passing 10% of the best individuals of the population from one generation to the next, without making any change to them. The procedure described from the selection operator was repeated for each generation, where

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the new population was generated with the same size of the initial population and the value of the objective function equal or higher at each generation. This new obtained population resulted in a representative sequence of the production sequencing and temperature data as well as batch time for each of the wastes, in a pre-defined time horizon. The proposed algorithm also incorporated a rescheduling strategy to face the frequent changes of products and price and demand variations, becoming dynamic. A generative type of rescheduling was utilised (Bael, 1999).

3.7. Economic analysis It is possible to evaluate the attraction for investment in a multiproduct pyrolysis plant with the result of the profit obtained in the period as well as equipment capacity, through the calculation of the income rate over the investment (ROI).

4. Results The research carried out in the industrial city of Caxias do Sul, located in South of Brazil has shown that the main generated industrial solid wastes, for their volume as well as for their degree of perilousness, were: Polypropylene (PP), Acrylonitrile Butadiene Styrene (ABS) and polyurethanic paint (paint sludge), with the following generation rate: 7.500, 2.500 and 30.000 Kg/month, respectively. Pyrolysis reactions were carried out over the conditions shown in Table 1. Table 1. Operational conditions of pyrolysis reactions Run Temperature of reaction (°C) Time of reaction (min)

1 450 10

2 650 10

3 450 90

4 650 90

5 550 50

6 550 50

In these conditions, for the three types of feeding, the reaction generated a gaseous phase comprising of CH4, C2H6, C2H4, C2H2, C3H8 and C3H6. The composition of the liquid phase was also determined by gas chromatography and, initially, divided into functional groups due to the great variety of liquid products. Table 2 shows the obtained results for paint waste (left column) and PP waste (right column). ABS waste resulted basically in 100% of aromatic compounds in all experiments. Table 2. Composition by chemical class – PP and paint waste Chemical Class 1 Parafins Olefins Naftenics Aromatics Chetone Éster Alcohol Others

2

4,68 2,7 5,66 2,97 5,8 4,82 0 4,19 0 17,59 43,8 0 33,43 0 1,04 15,81 63,8 42,41 65,98 47,85 1,42 16,61 0,00 10,6 1,78 5,34 13,11 0,00 11,34 2,99 5,64 2,84 0,00 7,23 2,29 18,49 0,95 14,3 1,87 19,62

Run 3 1,61 0 0 67,23 11,62 10,84 7,93 0,77

%(w/w) 4 5,59 0 3,35 8,52 0 3,48 13,55 0 32,11 54,49 71,1 40,94 0,00 8,43 0,00 0,00 9,16 0,49 0,00 10,08 1,11 17,85 1,23 18,56

5 4,91 0 0 63,35 11,99 14,53 4,36 0,86

6 5,01 11,13 36,48 33,75 0,00 0,00 0,55 13,08

0 0 0 73,81 6,41 10,99 7,63 1,16

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The added value to the produced oil depends on its classification according to experiments of the Agência Nacional de Petróleo (Brazilian National Petroleum Agency). Results in Table 3 show that all produced oils have calorific power, viscosity and specific mass of combustible oil; however, the flashing point of the obtained oil from the paint waste characterizes it as inflammable. Table 3. Physical Properties of oils produced by pyrolysis Sample of oil

Inferior Calorific Viscosity [cSt] Power [cal/g] Paint Sludge1 9.222,20 64,18 Paint Sludge2 9.296,44 28,61 9.725,14 30,31 Paint Sludge3 ABS1 11.087 56,83 10.456,10 43,32 ABS2 ABS3 11.363,72 54,72 10.458,97 71,73 PP1 PP2 11.149,43 44,79 10.883,85 45,83 PP3 1 450ºC, 90 min., 2 550ºC, 90 min., 3 650ºC, 90 min.

Specific Mass [g/cm³] 0,888 0,877 0,916 0,760 0,767 0,771 0,775 0,933 0,794

Flash Point [°C] 25 23 20 65 75 76 65 67 67

The yield of the produced oils by the pyrolysis reaction was defined as the ratio between the generated mass of oil and the fed mass of solid waste. Figures 1and 2 show the influence of temperature and batch time over the yield of the obtained oil from the ABS e paint sludge wastes.

Figura 1 – Influence of temperature and batch time over the yield of the obtained oil from ABS waste

Figura 2 - Influence of temperature and batch time over the yield of the obtained oil from paint sludge waste The obtained curve for PP was similar to the ABS one. The yield data as a function of time were experimentally adjusted.

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The experimental system allowed the online acquisition of temperature, batch time and tension data. The tension data was utilised to heat an electrical resistor of 16.55 ohms surrounding the reactor. It was possible to measure the electrical power every 5 seconds and therefore determine the consumed energy from the beginning of the experiment until the reaction temperature (transient energy) as well as the necessary energy to keep the reaction temperature (permanent energy). The transient energy was described as a function of fed mass of waste and variation of the reaction temperature from the beginning of heating until the reaction temperature was stabilised, which was considered stable in the range of set point ±5ºC. Ten distinct experimental runs were performed, changing the mass from 0 to 800 grams and temperature from 400 to 650ºC. Electrical tension data were converted to power knowing the value of electrical resistance and the conversion of power to energy was carried out integrating the power curve versus time using the Simpson’s rule. The permanent energy was determined as a function of fed mass of waste, reaction time and average reaction temperature, which was considered as the average temperature in the steady state, represented by the interval of the temperature of the set point ± 5ºC, during 90 minutes. The total quantity of energy consumed to processes the waste i (Eci) was the sum of transient and permanent energy. With the mass and composition of the obtained gases, the combustion heat of the mixture (Epi) was determined over the conditions shown in Table 1. In all cases the adjust of experimental data showed a correlation coefficient around 0,99 and the minimum percentage of variance explained by the model was 98,6%. Data from Table 4 were utilised to simulate production planning and sequencing as well as the operational conditions of pyrolysis of industrial solid wastes, using Genetic Algorithms in real codification as optimization method. Table 4. Data utilised in simulation

Specific mass (Kg/L) Specific heat (J/KgºC) Maximum waste offer (Kg/mês)

Maximum oil demand (Kg/mês) Unit price (R$/Kg) Unit price (R$/Kg) Restrictions of model Ht = 12.000 min. 5min ≤ tRi ≤ 90min 450ºC ≤ TRi ≤650ºC

Physical Properties ABS Paint Sludge PP 1 0,935 0,910 1.465,38 1.690,984 1.590,984 2.500 30.000 7.500 Economic Data Combustible inflammable 150.000 30.000 0,5 0,2 combustible gas 0,69 Genetic Parameters crossover rate = 0,95 mutation rate = 0,003 type of scaling = sigma type of crossover = extrapoler Size of population = 100 individuals Number of generations = 2.000

The obtained results for production planning and after rescheduling (values in parentheses) are shown in Table 5. The rescheduling proposed a reduction in the price of combustible gas from R$ 0,69/Kg to R$ 0,40/Kg.

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Table 5. Summary of production planning for a population of 100 individuals Number of batches/mouth Amount of processed waste (Kg/month) Income from waste (R$/month) Amount of oil sold (Kg/month) Income from oil selling(R$/month) Combustible gas income (R$/month) Maximum profit (R$/mouth) Execution time (h:min:s)

ABS 15 (15) 2.442 (2.442)

Paint sludge 198 (183) 2,998E4 (2,815E4)

PP 38 (50) 5.629 (7.407)

366 (366) 11.393 (10.699) 844 (1.111) inflammable combustible 5.497 (5.188) 5.772 (6.857) 1.099,4 (1.036) 2.886 (3.428) 5.812 (2.556) 19.200 (17.810) 00:35:16 (00:33:57)

It is observed that before rescheduling, ABS and paint wastes were processed until the maximum allowed by the restrictions of waste offer. Most of the profit was due to the income from paint waste purchase and selling of combustible gas. Lowering the price of combustible gas from R$ 0,69/Kg to 0,40/Kg, the most significant difference is then in the income from selling combustible oil, that therefore surpass the income from gas selling, leading to an increase in processing of PP waste until its maximum limit, with the reduction of the number of paint waste batches. Using the monthly profit obtained in the simulation and a total investment of R$ 88.489.00, the monthly rate of income over the investment was 21,7% per month.

5. Conclusions The utilised optimization methodology, with the simultaneous determination of planning and scheduling, is flexible allowing rescheduling to absorb the variations of market demand, production capacity and operational conditions, in a viable computational time. It can be used for several wastes where products yield and involved energy are determined without previously knowing the kinetics and heat of the pyrolysis reactions. The pyrolysis plant has shown economically viable, producing combustible oil and gas, being energetically selfsustainable and with most of the income generated from the production of combustible gas.

Nomenclature Sets:

Dminj= minimum demand of products generated by

i = 1,...,R wastes;

waste i;

j = 1,...,P oils;

Ht= length of period t;

k = 1,..., Nbit batches of waste i in time period t;

Omaxit = maximum offer of waste i in time period t.

t = 1,...,T time periods.

PPjt= market price of oil j in period t;

Parameters:

PRit= market price of waste i in time period t;

Ct= unit cost of combustile gas in time period t;

Tmaxi= maximum temperature to process waste i;

Dmaxj= maximum demand of products generated

tmaxi= maximum time to process the waste i;

by waste i;

Tmini= minimum temperature to process the waste i;

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tmini= minimum time to process the waste i;

GPit= total quantity of combustible gas produced of

Ts= setup time;

waste i in time period t;

Variables: CTPt = total cost of production in period t; Eci=total energy consumed to processes the waste i; Epi=total energy produced from waste i; GCit= total quantity of combustible gas consumed to processes the waste i in time period t; GLt= liquid mass of combustible gas in period t;

Nbit= number of batches of waste i in time period t; QPjt= total quantity of product j produced in period t; QRit = total quantity of waste i processed in time period t; ti = time of one batche of waste i; TRi = temperature of reaction of waste i; tRi = time of reaction of waste i; xj =yield of oil j;

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